PETRAS Publications

@article{art-cath_artificial_2017,

title = {Artificial Intelligence and the `Good Society': the US, EU, and UK approach},

author = {Corinne Cath and Sandra Wachter and Brent Daniel Mittelstadt and Mariarosaria Taddeo and Luciano Floridi},

url = {https://doi.org/10.1007%2Fs11948-017-9901-7},

doi = {10.1007/s11948-017-9901-7},

year  = {2017},

date = {2017-03-28},

publisher = {Springer Science and Business Media LLC},

abstract = {In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.},

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@article{art-taddeo_data_2017,

title = {Data Philanthropy and Individual Rights},

author = {Mariarosaria Taddeo},

url = {https://doi.org/10.1007%2Fs11023-017-9429-2},

doi = {10.1007/s11023-017-9429-2},

year  = {2017},

date = {2017-03-16},

volume = {27},

number = {1},

pages = {1--5},

publisher = {Springer Science and Business Media LLC},

abstract = {Data Philanthropy-the donation of data from private companies-is becoming increasing more popular, as corporations, like Genentech and PfizerFootnote1 donate their data, and international organisations, like the UN, start to create the infrastructure to facilitate the sharing of corporate-owned data (Kirkpatrick 2013). 

 However, competing tensions on data control and ownership (Kaisler et al. 2013; Andrejevic 2014; Kostkova et al. 2016), limited technical understanding, and the lack of adequate frameworks for coordination and governance (Mayer-Sch\"{o}nberger and Kenneth 2013; Vayena et al. 2015) pose serious obstacles to the attempts to share data among different actors, especially when these include private corporations. This was the case, for example, in 2014 during the Ebola crisis in West Africa, when gaining access to mobile network operators' data on population movement would have facilitated tracking the spreading of the disease, but proved to be impossible, because of issues concerning commercial interests, users' privacy, national security, as well as regulatory uncertainty. 

 Understanding how to access these data and how to harness their value for the common good is one of the main challenges of this decade.},

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@article{art-speed_centre_2017,

title = {Centre for design informatics},

author = {Chris Speed and Jon Oberlander},

url = {https://doi.org/10.1145%2F3047400},

doi = {10.1145/3047400},

year  = {2017},

date = {2017-02-21},

volume = {24},

number = {2},

pages = {18--21},

publisher = {Association for Computing Machinery (ACM)},

abstract = {The Centre for Design Informatics provides a platform in which design and data science can mix. As a team, we are interested in the emerging field of human-data interactions and developing ways for design to engage with the complexity of digital economic systems. Through a combination of methods from both the humanities and sciences, the lab offers design-centered solutions for cultural, commercial, and civic sectors, often resulting in new forms of visualizing, experiencing, and interpreting data. The strategic view of Design Informatics is to provide methodologies and solutions that allow organizations to better understand the value of their data in ways that are commensurate with the values of the stakeholders within each network.},

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@article{art-mittelstadt_individual_2017,

title = {From Individual to Group Privacy in Big Data Analytics},

author = {Brent Daniel Mittelstadt},

url = {https://doi.org/10.1007%2Fs13347-017-0253-7},

doi = {10.1007/s13347-017-0253-7},

year  = {2017},

date = {2017-02-11},

volume = {30},

number = {4},

pages = {475--494},

publisher = {Springer Science and Business Media LLC},

abstract = {Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics (as in big data analytics) has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that does not necessarily align with classes or attributes already protected by privacy and anti-discrimination law or addressed in fairness- and discrimination-aware analytics. Individuals are linked according to offline identifiers (e.g. age, ethnicity, geographical location) and shared behavioural identity tokens, allowing for predictions and decisions to be taken at a group rather than individual level. This article examines the ethical significance of such ad hoc groups in analytics and argues that the privacy interests of algorithmically assembled groups in inviolate personality must be recognised alongside individual privacy rights. Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf. Group privacy is proposed as a third interest to balance alongside individual privacy and social, commercial and epistemic benefits when assessing the ethical acceptability of analytics platforms.},

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@article{art-diddio_manyopt_2017,

title = {Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving},

author = {Andrea Callia D'Iddio and Michael Huth},

url = {http://arxiv.org/abs/1702.01332v1},

year  = {2017},

date = {2017-02-04},

journal = {arxiv:1702.01332},

abstract = {Optimization of Mixed-Integer Non-Linear Programming (MINLP) supports important decisions in applications such as Chemical Process Engineering. But current solvers have limited ability for deductive reasoning or the use of domain-specific theories, and the management of integrality constraints does not yet exploit automated reasoning tools such as SMT solvers. This seems to limit both scalability and reach of such tools in practice. We therefore present a tool, ManyOpt, for MINLP optimization that enables experimentation with reduction techniques which transform a MINLP problem to feasibility checking realized by an SMT solver. ManyOpt is similar to the SAT solver ManySAT in that it runs a specified number of such reduction techniques in parallel to get the strongest result on a given MINLP problem. The tool is implemented in layers, which we may see as features and where reduction techniques are feature vectors. Some of these features are inspired by known MINLP techniques whereas others are novel and specific to SMT. Our experimental results on standard benchmarks demonstrate the benefits of this approach. The tool supports a variety of SMT solvers and is easily extensible with new features, courtesy of its layered structure. For example, logical formulas for deductive reasoning are easily added to constrain further the optimization of a MINLP problem of interest.},

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@article{art-maxwell_story_2017,

title = {Story Blocks: Reimagining Narrative Through the Blockchain},

author = {Deborah Maxwell and Chris Speed and Larissa Pschetz},

url = {https://doi.org/10.1177%2F1354856516675263},

doi = {10.1177/1354856516675263},

year  = {2017},

date = {2017-01-24},

volume = {23},

number = {1},

pages = {79--97},

publisher = {SAGE Publications},

abstract = {Digital technology is changing, and has changed the ways we create and consume narratives, from moving images and immersive storyworlds to digital long-form and multi-branched story experiences. At the same time, blockchain, the technology that underpins cryptocurrencies such as Bitcoin, is revolutionizing the way that transactions and exchanges occur. As a globally stored and collaboratively written list of all transactions that have ever taken place within a given system, the blockchain decentralizes money and offers a platform for its creative use. There are already examples of blockchain technologies extending beyond the realm of currency, including the decentralization of domain name servers that are not subject to government takedown and identity management and governance. By framing key blockchain concepts with past and present storytelling practices, this article raises questions as to how the principles and implementation of such distributed ledger technologies might be used within contemporary writing practices - that is, can we imagine stories as a currency or value system? We present three experiments that draw on some of the fundamental principles of blockchain and Bitcoin, as an instantiation of a blockchain implemented application, namely, (1) the ledger, (2) the blocks and (3) the mining process. Each low-fi experiment was intentionally designed to be very accessible to take part in and understand and all were conducted as discrete workshops with different sets of participants. Participants included a cohort of design students, technology industry and design professionals and writing and interaction design academics. Each experiment raised a different set of reflections and subsequent questions on the nature of digital, the linearity (or not) of narratives and collaborative processes.},

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@article{art-ahlfeldt_speed_2017,

title = {Speed 2.0: Evaluating Access to Universal Digital Highways},

author = {Gabriel Ahlfeldt and Pantelis Koutroumpis and Tommaso Valletti},

url = {https://doi.org/10.1093%2Fjeea%2Fjvw013},

doi = {10.1093/jeea/jvw013},

year  = {2017},

date = {2017-01-09},

volume = {15},

number = {3},

pages = {586--625},

publisher = {Oxford University Press (OUP)},

abstract = {This paper shows that having access to a fast Internet connection is an important determinant of capitalization effects in property markets. Our empirical strategy combines a boundary discontinuity design with controls for time-invariant effects and arbitrary macroeconomic shocks at a very local level to identify the causal effect of broadband speed on property prices from variation that is plausibly exogenous. Applying this strategy to a micro data set from England between 1995 and 2010 we find a significantly positive effect, but diminishing returns to speed. Our results imply that disconnecting an average property from a high-speed first-generation broadband connection (offering Internet speed up to 8 Mbit/s) would depreciate its value by 2.8%. In contrast, upgrading such a property to a faster connection (offering speeds up to 24 Mbit/s) would increase its value by no more than 1%. We decompose this effect by income and urbanization, finding considerable heterogeneity. These estimates are used to evaluate proposed plans to deliver fast broadband universally. We find that increasing speed and connecting unserved households pass a cost-benefit test in urban and some suburban areas, whereas the case for universal delivery in rural areas is not as strong. (JEL: L1, H4, R2)},

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@article{art-coulton_design_2017,

title = {Design Fiction: Anticipating Adoption},

author = {Paul Coulton and Joseph Galen Lindley},

url = {https://doi.org/10.1109%2Fmprv.2017.5},

doi = {10.1109/mprv.2017.5},

year  = {2017},

date = {2017-01-05},

volume = {16},

number = {1},

pages = {43--47},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {This tutorial highlights the potential of design fiction with Game of Drones, a fictional user trial of an imagined Drone Enforcement System. The authors explore a potential future use of drones for civic enforcement activities and advance a program for developing design fiction as a research method. This method provides a means for exploring the societal, technological, and political nuances of possible futures so researchers can better consider possible adoption pathways for emerging technologies. This tutorial is part of a special issue on drones.},

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@article{art-li_advance_2017,

title = {ADvaNCE \textendash Efficient and Scalable Approximate Density-Based Clustering Based on Hashing},

author = {Tianrun Li and Thomas Heinis and Wayne Luk},

url = {https://doi.org/10.15388%2Finformatica.2017.122},

doi = {10.15388/informatica.2017.122},

year  = {2017},

date = {2017-01-01},

volume = {28},

number = {1},

pages = {105--130},

publisher = {Vilnius University Press},

abstract = {Analysing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, current approaches for data analysis are no longer efficient. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time: the more data points are in the dataset, the bigger the share of time needed for distance calculations. 

 Crucial to the data analysis and clustering process, however, is that it is rarely straightforward: instead, parameters need to be determined and tuned first. Entirely accurate results are thus rarely needed and instead we can sacrifice little precision of the final result to accelerate the computation. In this paper we develop ADvaNCE, a new approach based on approximating DBSCAN. More specifically, we propose two measures to reduce distance calculation overhead and to consequently approximate DBSCAN: (1) locality sensitive hashing to approximate and speed up distance calculations and (2) representative point selection to reduce the number of distance calculations. 

 The experiments show that the resulting clustering algorithm is more scalable than the state-of-the-art as the datasets become bigger. Compared with the most recent approximation technique for DBSCAN, our approach is in general one order of magnitude faster (at most 30× in our experiments) as the size of the datasets increase.},

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@article{art-ravi_deep_2017a,

title = {Deep Learning for Health Informatics},

author = {Daniele Ravi and Charence Wong and Fani Deligianni and Melissa Berthelot and Javier Andreu-Perez and Benny P. L. Lo and Guang-Zhong Yang},

url = {https://doi.org/10.1109%2Fjbhi.2016.2636665},

doi = {10.1109/jbhi.2016.2636665},

year  = {2016},

date = {2016-12-29},

volume = {21},

number = {1},

pages = {4--21},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.},

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@article{art-floridi_faultless_2016,

title = {Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions},

author = {Luciano Floridi},

editor = {Mariarosaria Taddeo and Luciano Floridi},

url = {https://doi.org/10.1098%2Frsta.2016.0112},

doi = {10.1098/rsta.2016.0112},

issn = {1471-2962},

year  = {2016},

date = {2016-12-28},

volume = {374},

number = {2083},

pages = {20160112},

publisher = {The Royal Society},

series = {The ethical impact of data science},

abstract = {The concept of distributed moral responsibility (DMR) has a long history. When it is understood as being entirely reducible to the sum of (some) human, individual and already morally loaded actions, then the allocation of DMR, and hence of praise and reward or blame and punishment, may be pragmatically difficult, but not conceptually problematic. However, in distributed environments, it is increasingly possible that a network of agents, some human, some artificial (e.g. a program) and some hybrid (e.g. a group of people working as a team thanks to a software platform), may cause distributed moral actions (DMAs). These are morally good or evil (i.e. morally loaded) actions caused by local interactions that are in themselves neither good nor evil (morally neutral). In this article, I analyse DMRs that are due to DMAs, and argue in favour of the allocation, by default and overridably, of full moral responsibility (faultless responsibility) to all the nodes/agents in the network causally relevant for bringing about the DMA in question, independently of intentionality. The mechanism proposed is inspired by, and adapts, three concepts: back propagation from network theory, strict liability from jurisprudence and common knowledge from epistemic logic. 

 

This article is part of the themed issue 'The ethical impact of data science'.},

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@article{art-taddeo_data_2016,

title = {Data philanthropy and the design of the infraethics for information societies},

author = {Mariarosaria Taddeo},

editor = {Mariarosaria Taddeo and Luciano Floridi},

url = {https://doi.org/10.1098%2Frsta.2016.0113},

doi = {10.1098/rsta.2016.0113},

issn = {1471-2962},

year  = {2016},

date = {2016-12-28},

volume = {374},

number = {2083},

pages = {20160113},

publisher = {The Royal Society},

series = {The ethical impact of data science},

abstract = {In mature information societies, sharing data is increasingly recognized as a crucial means to foster their development. However, competing tensions on data control and ownership, limited technical understanding, and the lack of an adequate governance framework pose serious challenges to attempts to share data among different actors. Data philanthropy, understood as the donation of data from both individuals and private companies, has been proposed as means to meet these challenges. While at first sight data philanthropy may seem an uncontroversial phenomenon, a closer analysis reveals a bewildering network of problems. In this article, I analyse the role of data philanthropy in contemporary societies and the moral problems that it yields. I argue that the solution to these problems rests on the understanding of the infraethical nature of data philanthropy and on the design of an ethical framework encompassing the right infraethics and the right ethics. This is a framework able to address the changes brought about by the information revolution and to harness the opportunities that these pose for the prosperity of current and future information societies. 

 This article is part of the themed issue 'The ethical impact of data science'.},

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@article{art-floridi_what_2016,

title = {What is data ethics?},

author = {Luciano Floridi and Mariarosaria Taddeo},

editor = {Mariarosaria Taddeo and Luciano Floridi},

url = {https://doi.org/10.1098%2Frsta.2016.0360},

doi = {10.1098/rsta.2016.0360},

issn = {1471-2962},

year  = {2016},

date = {2016-12-28},

volume = {374},

number = {2083},

pages = {20160360},

publisher = {The Royal Society},

series = {The ethical impact of data science},

abstract = {This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations\textemdashthe interactions among hardware, software and data\textemdashrather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments. 

 This article is part of the themed issue 'The ethical impact of data science'.},

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@article{art-ravi_deep_2017,

title = {A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices},

author = {Daniele Ravi and Charence Wong and Benny P. L. Lo and Guang-Zhong Yang},

url = {https://doi.org/10.1109%2Fjbhi.2016.2633287},

doi = {10.1109/jbhi.2016.2633287},

year  = {2016},

date = {2016-12-23},

volume = {21},

number = {1},

pages = {56--64},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.},

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@article{art-watson_petras_2016,

title = {PETRAS: Cyber Security Of The Internet Of Things},

author = {Jeremy Daniel McKendrick Watson and Emil C. Lupu},

url = {https://crestresearch.ac.uk/comment/petras-cyber-security-of-the-internet-of-things/},

year  = {2016},

date = {2016-12-20},

abstract = {PETRAS is a research hub to fill knowledge gaps and promote safe and secure use of the Internet of Things. 

 At the beginning of 2016, the PETRAS Hub consortium of nine leading UK universities was awarded £9.8m by the Engineering and Physical Science Research Council (EPSRC). PETRAS brings the universities together with around 50 user partners representing both the private and public sectors. 

 Strategic review of IoT 

 Following a strategic review by the UK Government, 'The Internet of Things: making the most of the Second Digital Revolution' was published in 2014. It emphasised the economic importance of the Internet of Things (IoT), which would only be realised by ensuring its cybersecurity and trustworthiness while not standing in the way of vibrant technical and business development. The government response was to create the £40m IoTUK initiative, which funds the PETRAS hub amongst other initiatives. 

 Principles of the PETRAS hub 

 The review highlighted a knowledge and capability gap in the ability to look at IoT (or indeed other) cybersecurity from an integrated socio-technical viewpoint. Collaborative thinking across social and physical science disciplines was needed from project identification to execution. This principle has guided the vision for PETRAS. 

 PETRAS stands for Privacy, Ethics, Trust, Reliability, Acceptability and Security - headings that have relevance to both technical and social science. They are all important in ensuring the successful adoption of the Internet of Things. The PETRAS hub is founded on these six themes and emphasises in equal measure, the physical and social science aspects of the adoption of new IoT technology. The academic partners are made up of a cross-disciplinary Hub team of UCL, Imperial College, Oxford, Leicester and Warwick, augmented by four Spoke contributors at Surrey, Southampton, Cardiff and Edinburgh, who provide specialist contributions. Additionally, PETRAS boasts a large cohort of user and research partners in the private sector (ranging across banking, through healthcare to mobile telecommunications), the public and NGO sectors. 'Impact Champions' working in the PETRAS management team ensure good bidirectional connections between these and the academic partners. 

 Planned projects 

 In order to best represent and investigate the opportunities and challenges of the wide span of IoT applications, the partners have created a project structure which feeds into the generic themes of interest; Privacy \& Trust, Safety \& Security, Harnessing Economic Value, Standards, Governance \& Public Policy, and Adoption \& Acceptability. A number of projects will provide evidence under these headings; these we have grouped by type or sector into areas of applications or 'Constellations'. Around 20 initial projects cover the constellation themes. PETRAS has been designed so that further internal calls for projects can be shaped to fill the research gaps identified with user partners and then consolidate the research outcomes into concrete demonstrators. PETRAS plans to become the go-to place for research in cybersecurity of the IoT in the UK by creating an inclusive technical and social platform for innovation that will continue beyond the end of the funded period. 

 Examples of projects within these constellations include: 

 Transport \& Mobility where projects will include smart street planning, pricing and road maintenance, and security and privacy solutions for communicating autonomous and semi-autonomous cars and infrastructures. 

 The Health \& Care constellation will include modelling and analysis for body sensor networks, security mechanisms for miniaturised low power chips, and an investigation of the factors of user trust in medical applications of IoT. 

 Design \& Behaviour explores the role Design plays in influencing the adoption of IoT. In particular, how Design and Engineering can actively encourage or discourage behaviours so that Privacy and Trust are enhanced and adoption is promoted. 

 Projects under the Infrastructure heading look, from a policy angle, at approaches in various countries and across borders to manage IoT threats and increased attack surfaces. These projects include tools to analyse threats in many contexts. 

 Identification constellation projects deal with the trustworthiness of identification systems and evaluating identification technologies, protocols, and procedures alongside privacy strategies, to identify robust solutions that deliver a balance between identifiability and privacy of IoT technology. 

 Supply \& Control Systems projects cover secure IoT-augmented control systems for industry and buildings and explore the economic value of IoT data in cyber-physical supply chains. 

 The Ambient Environments constellation investigates the impact of security on adaptability within cross-layered network-wide protocols for low powered IoT devices. A combination of 'In the Wild' experiments on the Olympic Park and focus groups will explore the boundaries of privacy, trust and personalisation.},

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@article{art-mittelstadt_ethics_2016,

title = {The ethics of algorithms: Mapping the debate},

author = {Brent Daniel Mittelstadt and Patrick Allo and Mariarosaria Taddeo and Sandra Wachter and Luciano Floridi},

url = {https://doi.org/10.1177%2F2053951716679679},

doi = {10.1177/2053951716679679},

year  = {2016},

date = {2016-12-01},

volume = {3},

number = {2},

pages = {1--21},

publisher = {SAGE Publications},

abstract = {In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.},

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@article{art-johnblythe_how_2016,

title = {How to save the Internet of Things from cyber attacks --- with psychology},

author = {John Blythe M. and Carmen Lefevre},

url = {https://theconversation.com/how-to-save-the-internet-of-things-from-cyber-attacks-with-psychology-68608},

year  = {2016},

date = {2016-11-14},

abstract = {Two scientists were recently able to take over the lights of an office building using a drone and some clever computer hacking. They demonstrated how ``smart'' lightbulbs connected to the internet were vulnerable to a virus that could spread from one infected light to any bulb in range. The researchers flew a drone up to the building, transmitted a signal that hacked into one light and then took control of the whole floor. In theory, such an attack could be used to take out the lights of an entire city, if smartbulbs were to become commonplace. 

 These bulbs are just one example of devices that can be connected to the Internet of Things (IoT). The IoT refers to any everyday object with the ability to collect to and exchange data over the internet. The technology can allow you to remotely and automatically control the heating, lighting, sound-system and other devices in your home, based on your normal routine. 

 But these devices are also vulnerable to cyber attacks. The lightbulb example may have been a research experiment, but in a major attack recently, hundreds of thousands of IoT devices were captured by hackers and used to bring down many popular websites. So we need to make these objects more secure. One way to do this is to use psychology to understand users' capabilities and motivations and try to change people's behaviour.},

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@article{art-perera_valorising_2016,

title = {Valorising the IoT Databox: creating value for everyone},

author = {Charith Perera and Susan Y. L. Wakenshaw and Tim Baarslag and Hamed Haddadi and Arosha K. Bandara and Richard Mortier and Andy Crabtree and Irene C. L. Ng and Derek McAuley and Jon Crowcroft},

url = {https://doi.org/10.1002%2Fett.3125},

doi = {10.1002/ett.3125},

year  = {2016},

date = {2016-11-11},

volume = {28},

number = {1},

pages = {e3125},

publisher = {Wiley},

abstract = {The Internet of Things is expected to generate large amounts of heterogeneous data from diverse sources including physical sensors, user devices and social media platforms. Over the last few years, significant attention has been focused on personal data, particularly data generated by smart wearable and smart home devices. Making personal data available for access and trade is expected to become a part of the data-driven digital economy. In this position paper, we review the research challenges in building personal Databoxes that hold personal data and enable data access by other parties and potentially thus sharing of data with other parties. These Databoxes are expected to become a core part of future data marketplaces.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-chen_pervasive_2016,

title = {Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review},

author = {Shanshan Chen and John Lach and Benny P. L. Lo and Guang-Zhong Yang},

url = {https://doi.org/10.1109%2Fjbhi.2016.2608720},

doi = {10.1109/jbhi.2016.2608720},

year  = {2016},

date = {2016-09-22},

volume = {20},

number = {6},

pages = {1521--1537},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics - hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-zhao_rapid_2017,

title = {Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing},

author = {Cong Zhao and Shusen Yang and Xinyu Yang and Julie A. McCann},

url = {https://doi.org/10.1109%2Ftmc.2016.2611575},

doi = {10.1109/tmc.2016.2611575},

year  = {2016},

date = {2016-09-20},

volume = {16},

number = {7},

pages = {2008--2022},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate large-scale crowdsourcing tasks (e.g., urban sensing and collaborative computing). Many applications in this domain require Device-to-Device (D2D) communications between participating devices for interactive operations such as task collaborations and file transmissions. Considering the private participating devices and their opportunistic encountering behaviors, it is highly desired to establish secure and trustworthy D2D connections in a fast and autonomous way, which is vital for implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves user-transparent secure D2D connections and reliable peer device selections for trustworthy D2D communications. Through rigorous analysis, we demonstrate the effectiveness and security intensity of TDP in theory. The performance of TDP is evaluated based on both real-world prototype experiments and extensive trace-driven simulations. Evaluation results verify our theoretical analysis and show that TDP significantly outperforms existing approaches in terms of pairing speed, stability, and security.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-jhumka_neighborhood_2016,

title = {Neighborhood View Consistency in Wireless Sensor Networks},

author = {Arshad Jhumka and Luca Mottola},

url = {https://doi.org/10.1145%2F2901296},

doi = {10.1145/2901296},

year  = {2016},

date = {2016-07-26},

volume = {12},

number = {3},

pages = {1--41},

publisher = {Association for Computing Machinery (ACM)},

abstract = {Wireless sensor networks (WSNs) are characterized by localized interactions, that is, protocols are often based on message exchanges within a node's direct radio range. We recognize that for these protocols to work effectively, nodes must have consistent information about their shared neighborhoods. Different types of faults, however, can affect this information, severely impacting a protocol's performance. We factor this problem out of existing WSN protocols and argue that a notion of neighborhood view consistency (NVC) can be embedded within existing designs to improve their performance. To this end, we study the problem from both a theoretical and a system perspective. We prove that the problem cannot be solved in an asynchronous system using any of Chandra and Toueg's failure detectors. Because of this, we introduce a new software device called pseudocrash failure detector (PCD), study its properties, and identify necessary and sufficient conditions for solving NVC with PCDs. We prove that, in the presence of transient faults, NVC is impossible to solve with any PCDs, thus define two weaker specifications of the problem. We develop a global algorithm that satisfies both specifications in the presence of unidirectional links, and a localized algorithm that solves the weakest specification in networks of bidirectional links. We implement the latter atop two different WSN operating systems, integrate our implementations with four different WSN protocols, and run extensive micro-benchmarks and full-stack experiments on a real 90-node WSN testbed. Our results show that the performance significantly improves for NVC-equipped protocols; for example, the Collection Tree Protocol (CTP) halves energy consumption with higher data delivery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-lo_transforming_2016,

title = {Transforming Health Care: Body Sensor Networks, Wearables, and the Internet of Things},

author = {Benny P. L. Lo and Henry Ip and Guang-Zhong Yang},

url = {https://doi.org/10.1109%2Fmpul.2015.2498474},

doi = {10.1109/mpul.2015.2498474},

year  = {2016},

date = {2016-01-20},

volume = {7},

number = {1},

pages = {4--8},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {This paper talks about body sensor networks, wearables, and the Internet of Things.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-lovett_mobile_2021,

title = {Mobile Communications Technologies in Tree Time: The Listening Wood},

author = {Leah Lovett and Duncan Hay and Andy Hudson-Smith and Martin Jode},

doi = {10.1162/leon_a_02006},

volume = {54},

number = {2},

pages = {220--221},

publisher = {MIT Press - Journals},

abstract = {This article presents a practice-led investigation by a cross- disciplinary team of artists and computer scientists into the potential for mobile and digital communications technologies to engage visitors to London's Hampstead Heath with the histories of its veteran urban trees. Focusing on the application of Internet of Things (IoT) technologies within the arboreal environment for the digital poetic walk, The Listening Wood, it considers the reciprocal impact of "tree time" on the development of "slow tech."},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-ohara_explainable_2020,

title = {Explainable AI and the philosophy and practice of explanation},

author = {Kieron O'Hara},

url = {https://doi.org/10.1016%2Fj.clsr.2020.105474},

doi = {10.1016/j.clsr.2020.105474},

volume = {39},

pages = {105474},

publisher = {Elsevier BV},

abstract = {Considerations of the nature of explanation and the law are brought together to argue that computed accounts of AI systems' outputs cannot function on their own as explanations of decisions informed by AI. The important context for this inquiry is set by Article 22(3) of GDPR. The paper looks at the question of what an explanation is from the point of view of the philosophy of science - i.e. it asks not what counts as explanatory in legal terms, or what an AI system might compute using provenance metadata, but rather what explanation as a social practice consists in, arguing that explanation is an illocutionary act, and that it should be considered as a process, not a text. It cannot therefore be computed, although computed accounts of AI systems are likely to be important inputs to the explanatory process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-radanliev_data_2020,

title = {Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development - In the first wave of the Covid-19 pandemic},

author = {Petar Radanliev and David Charles De Roure and Rob Walton},

url = {https://doi.org/10.1016%2Fj.dsx.2020.06.063},

doi = {10.1016/j.dsx.2020.06.063},

volume = {14},

number = {5},

pages = {1121--1132},

publisher = {Elsevier BV},

abstract = {Background and aims 

 Covid-19 is a global pandemic that requires a global and integrated response of all national medical and healthcare systems. Covid-19 exposed the need for timely response and data sharing on fast spreading global pandemics. In this study, we investigate the scientific research response from the early stages of the pandemic, and we review key findings on how the early warning systems developed in previous epidemics responded to contain the virus. 

 Methods 

 We conducted data mining of scientific literature records from the Web of Science Core Collection, using the topics Covid-19, mortality, immunity, and vaccine. The individual records are analysed in isolation, and the analysis is compared with records on all Covid-19 research topics combined. The data records are analysed with commutable statistical methods, including R Studio's Bibliometrix package, and the Web of Science data mining tool. 

 Results 

 From historical analysis of scientific data records on viruses, pandemics and mortality, we identified that Chinese universities have not been leading on these topics historically. However, during the early stages of the Covid-19 pandemic, the Chinese universities are strongly dominating the research on these topics. Despite the current political and trade disputes, we found strong collaboration in Covid-19 research between the US and China. From the analysis on Covid-19 and immunity, we wanted to identify the relationship between different risk factors discussed in the news media. We identified a few clusters, containing references to exercise, inflammation, smoking, obesity and many additional factors. From the analysis on Covid-19 and vaccine, we discovered that although the USA is leading in volume of scientific research on Covid-19 vaccine, the leading 3 research institutions (Fudan, Melbourne, Oxford) are not based in the USA. Hence, it is difficult to predict which country would be first to produce a Covid-19 vaccine. 

 Conclusions 

 We analysed the conceptual structure maps with factorial analysis and multiple correspondence analysis (MCA), and identified multiple relationships between keywords, synonyms and concepts, related to Covid-19 mortality, immunity, and vaccine development. We present integrated and corelated knowledge from 276 records on Covid-19 and mortality, 71 records on Covid-19 and immunity, and 189 records on Covid-19 vaccine.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-anthi_adversarial_2021,

title = {Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems},

author = {Eirini Anthi and Lowri Williams and Matilda Rhode and Peter Burnap and Adam Wedgbury},

url = {https://doi.org/10.1016%2Fj.jisa.2020.102717},

doi = {10.1016/j.jisa.2020.102717},

volume = {58},

pages = {102717},

publisher = {Elsevier BV},

abstract = {The proliferation and application of machine learning-based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 6 and 11 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-webster_exploring_2020,

title = {Exploring the effects of environmental conditions and design choices on IoT systems using formal methods},

author = {Matt Webster and Michael J. Breza and Clare Dixon and Michael Fisher and Julie A. McCann},

url = {https://doi.org/10.1016%2Fj.jocs.2020.101183},

doi = {10.1016/j.jocs.2020.101183},

volume = {45},

pages = {101183},

publisher = {Elsevier BV},

abstract = {Wireless communication protocols are often used in critical applications, e.g., urban water supply networks or healthcare monitoring within the Internet of Things. It is essential that control software and protocols for such systems are verified to be both robust and reliable. The effects on the hardware caused by environmental conditions and the choice of parameters used by the protocol are among the largest obstacles to robustness and reliability in wireless systems. In this paper we use formal verification to verify that a wireless sensor network synchronization and dissemination protocol is not adversely affected by these factors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{art-wachter_why_2017,

title = {Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation},

author = {Sandra Wachter and Brent Daniel Mittelstadt and Luciano Floridi},

url = {https://doi.org/10.1093%2Fidpl%2Fipx005},

doi = {10.1093/idpl/ipx005},

volume = {7},

number = {2},

pages = {76--99},

publisher = {Oxford University Press (OUP)},

abstract = {Since approval of the European Union General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that a 'right to explanation' of all decisions made by automated or artificially intelligent algorithmic systems will be legally mandated by the GDPR once it is in force, in 2018. 

 However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a 'right to be informed'. 

 The ambiguity and limited scope of the 'right not to be subject to automated decision-making' contained in Article 22 (from which the alleged 'right to explanation' stems) raises questions over the protection actually afforded to data subjects. ApplicationsThese problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. 

 We propose a number of legislative steps that, if implemented, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}