Large Language Models (LLMs) offer potential benefits for increasing access to digital well-being support, yet their application raises important questions about risks and responsible implementation. This paper examines a critical, often overlooked, dimension of LLM safety: cultural and social alignment in underrepresented contexts. We investigate how LLM-mediated emotional support can be adapted for a specific cultural setting, using Saudi Arabia as a case study. We present CSESC, a Culturally Sensitive Emotional Support Chatbot, developed as a technology probe to explore user perceptions of culturally sensitive responses. Our adaptation process was grounded in emotional support frameworks and guided by multicultural guidelines and local expertise. User evaluations demonstrate that cultural alignment enhances users’ sense of relatedness, while also surfacing tensions between empathy and sociocultural norms. We discuss the notion of “minimum cultural alignment,” contributing to HCI literature on culturally responsive LLM design and broadening the understanding of LLM safety.

Young Saudi women (YSW) face increasing mental health challenges, often hidden behind social barriers and stigma. While digital support has been investigated in Western contexts to offer discreet alternatives, its application in Saudi Arabia remains understudied, particularly in addressing unique cultural needs. To address this gap, we conducted remote interviews with 20 YSW to explore their lived experiences and challenges with existing Arabic apps, followed by remote co-design workshops with 38 YSW to explore design preferences. Using thematic analysis, our findings reveal that many apps rely on surface-level translation and fail to align with users’ cultural values, limiting their adoption. Concerns about incomplete anonymity, fear of being discovered, and a lack of guidance further challenged their use. Participants also framed online privacy as a collective moral value, rather than an individual concern. We discuss these sociocultural sensitivities and propose design considerations for future design targeting Saudi and broader Arab audiences.

Decentralised social media (DSM) platforms such as Mastodon offer community-governed alternatives to corporate social networks but place substantial governance burdens on volunteer operators. As interest grows in applying artificial intelligence (AI) to support this work, little is known about whether DSM operators want AI, what roles they consider appropriate, and what governance boundaries they require. We conducted semi-structured interviews with 20 operators across Mastodon, Pixelfed, PeerTube, Lemmy, Pleroma, and Funkwhale, using generative feature probes and speculative scenarios to explore their perceptions of AI. Operators rejected AI as an autonomous actor, instead envisioning it as governance infrastructure that provides contextual intelligence, supports cross-instance coordination, and sustains community and moderator well-being. They also articulated strict boundaries rooted in DSM values, including human accountability, reversibility, transparency, community-centred configuration, and strong data-governance constraints. We contribute empirical insights and design implications for AI compatible with decentralised, federated social media.

As model parameter sizes scale into the billions and training consumes zettaFLOPs of computation, the reuse of Machine Learning (ML) assets and collaborative development have become increasingly prevalent in the ML community. These ML assets, including models, datasets, and software, may originate from various sources and be published under different licenses, which govern the use and distribution of licensed works and their derivatives. However, commonly chosen licenses, such as GPL and Apache, are software-specific and are not clearly defined or bounded in the context of model publishing. Meanwhile, the reused assets may also be under free-content licenses and model licenses, which pose a potential risk of license noncompliance and rights infringement within the model production workflow. In this paper, we address these challenges along two lines: 1) For ML workflow compliance, we propose ModelGo (MG) Analyzer, a tool that incorporates a vocabulary for ML workflow management and encoded license rules, enabling ontological reasoning to analyze rights granting and compliance issues. 2) For standardized model publishing, we introduce ModelGo Licenses, a set of modell-specific licenses that provide flexible options to meet the diverse needs of the ML community. MG Analyzer is built on Turtle language and Notation3 reasoning engine, envisioned as a first step toward Linked Open Data for ML workflow management. We have also encoded our proposed model licenses into rules and demonstrated the effects of GPL and other commonly used licenses in model publishing, along with the flexibility advantages of our licenses, through comparisons and experiments.

Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or software programming interfaces. This is the LanguageModels-as-a-Service (LMaaS) paradigm. In contrast with scenarios where full model access is available, as in the case of open-source models, such closed-off language models present specific challenges for evaluating, benchmarking, and testing them. This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, reproducibility, reliability, and trustworthiness of LMaaS. We systematically examine the issues that arise from a lack of information about language models for each of these four aspects. We conduct a detailed analysis of existing solutions, put forth a number of recommendations, and highlight directions for future advancements. On the other hand, it serves as a synthesized overview of the licences and capabilities of the most popular LMaaS.

Data and their processing have become an indispensable aspect for our society. Insights drawn from collective data make invaluable contribution to scientific, societal and communal research and business. However, there are increasing worries about privacy issues and data misuse, prompting the emergence of decentralised personal data stores (PDS) like Solid. However, existing PDS frameworks face challenges in ensuring data privacy when performing collective computation to combine data from multiple users. At a glance, Secure Multi-Party Computation (MPC) offers input secrecy protection while performing collective computation without relying on any single party. However, issues emerge when directly applying MPC in the context of PDS, particularly due to key factors like autonomy and decentralisation. In this work, we discuss the essence of this issue, identify the potential solution, and introduce a modular system architecture, Libertas, to integrate MPC with PDS like Solid, without requiring protocol-level changes. We introduce the paradigm shift from an 'omniscient' view to individual-based, user-centric view of trust and security, and discuss the threat model of Libertas. Two realistic use cases for collaborative data processing are used for evaluation, both for technical feasibility and empirical benchmark, highlighting its effectiveness in empowering gig workers and generating differentially private synthetic data. The results of our experiments underscore Libertas' linear scalability and provide valuable insights into compute optimisations, thereby advancing the state-of-the-art in privacy-preserving data processing practices. By offering practical solutions for maintaining both individual autonomy and privacy in collaborative data processing environments, Libertas contributes significantly to the ongoing discourse on privacy protection in data-driven decision-making contexts.

Is 'art' simply 'content' in the age of social media? What does art's creation, appreciation, and experience lose when art becomes content? This exploratory research employs a mixed methods approach encompassing interviews, co-design, and stakeholder reviews to critically examine the perceptions of artists, curators, and content creators regarding the distinction between 'art' and 'content.' Our participants reveal four characteristics essential for art experiences that we use to co-design new digital 'content' environments. The work concludes with content creators reviewing the characteristics and designs, offering their thoughts on the feasibility and potential future for 'art' on social media. We offer open-source access to all design and research materials. By exploring the divide between 'art' and 'content,' this research contributes insights and guidelines for developing alternative algorithmic experiences to support creativity on social media.

Social media has become a primary source of entertainment and education for children globally. While much attention has been given to children’s online well-being, a pressing concern often goes unnoticed: the pervasive data harvesting underlying social media and its manipulative impact on undermining children’s autonomy. In this paper, we present CHAITok, an Android mobile application designed to enhance children’s sense of autonomy over their data on social media. Through 27 user study sessions with 109 children aged 10–13, we offer insights into the current lack of data autonomy among children regarding their online information, and how we can foster children’s sense of data autonomy through a socio-technical journey. Our findings inspire design recommendations to respect children’s values, support children’s evolving autonomy, and design for children’s digital rights. We emphasize data autonomy as a fundamental right for children, call for further research, design innovation, and policy changes on this critical issue.

Mental health disorders are prevalent worldwide, yet they remain stigmatized, especially in the Middle East. While mHealth has the potential to circumvent traditional barriers, research on its application remains scarce in Arab countries. To address this gap, we conducted a mixed-methods study of mental health apps availability, adoption, and perceptions in the Kingdom of Saudi Arabia (KSA) where digital health transformation is rapidly progressing. We interviewed twelve psychiatrists and psychologists to elicit their views on local barriers and opportunities for digital mental health support. We further systematically reviewed the Saudi app market, analysing 110 Arabic mental health apps. Our findings indicate that whilst fear of stigma and cultural factors hindered help-seeking, the privacy and anonymity enabled by technology created new opportunities for accessing mental support in the KSA. We revealed tensions between experts’ professional and practical perspectives, explored technology-exacerbated challenges and provided considerations for improving Saudi digital mental healthcare experience.

Digital self-control tools (DSCTs) help people control their time and attention on digital devices, using interventions like distraction blocking or usage tracking. Most studies of DSCTs’ effectiveness have focused on whether a single intervention reduces time spent on a single device. In reality, people may require combinations of DSCTs to achieve more subjective goals across multiple devices. We studied how DSCTs can address individual needs of university students (n = 280), using a workshop where students reflect on their goals before exploring relevant tools. At 1-3 month follow-ups, 95% of respondents still used at least one type of DSCT, typically applied across multiple devices, and there was substantial variation in the tool combinations chosen. We observed a large increase in self-reported digital self-control, suggesting that providing a space to articulate goals and self-select appropriate DSCTs is a powerful way to support people who struggle to self-regulate digital device use.

Children today are deeply immersed in the online world, where their activities are routinely tracked, analysed, and monetised. This exposes them to various datafication risks, including harmful profiling, micro-targeting and behavioural engineering. Most existing measures focus on immediate online threats, rather than informing children about these implicit risks. In this paper, we present The KOALA Hero Toolkit, a hybrid toolkit designed to help children and parents jointly understand the datafication risks posed by their mobile apps. Through user studies involving 17 families we evaluate how the toolkit influenced families’ thought processes, perceptions and decision-making regarding mobile datafication risks. Our findings show that KOALA Hero supports families’ critical thinking and promotes family engagement. We identify future design recommendations for family support, featuring ideas such as integrating triggering moments and bonding moments in toolkit designs. This work provides timely inputs on global efforts aimed at addressing datafication risks and underscores the importance of strengthening legislative and policy enforcement of ethical data governance.

Privacy in apps is a topic of widespread interest because many apps collect and share large amounts of highly sensitive information. In response, the Chinese legislator introduced a range of new data protection laws over recent years, notably the Personal Information Protection Law (PIPL) in 2021. So far, there exists limited research on the impacts of these new laws on apps’ privacy practices. To address this gap, this paper analyses data collection in pairs of 634 Chinese iOS apps, one version from early 2020 and one from late 2021. Our work finds that many more apps now implement consent. Yet, those end-users that decline consent will often be forced to exit the app. Fewer apps now collect data without consent but many still integrate tracking libraries. Market concentration in app data collection has seen limited change. At the same time, there exists a larger number of influential and equal market participants than in the West. Among them, Apple was the only relevant foreign company. We see our findings characteristic of a first iteration at Chinese data regulation with room for improvement. With the help of enhanced technological capabilities, we expect increased enforcement of the new data rules. There is also room to refine the new laws and make them more targeted at mobile apps and the online sphere, particularly through clear and up-to-date technical specifications for software developers. As such, our findings could also be motivation for non-Chinese policy- and lawmakers to enhance their own data protection regimes.

Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or software programming interfaces. This is the LanguageModels-as-a-Service (LMaaS) paradigm. In contrast with scenarios where full model access is available, as in the case of open-source models, such closed-off language models present specific challenges for evaluating, benchmarking, and testing them. This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, reproducibility, reliability, and trustworthiness of LMaaS. We systematically examine the issues that arise from a lack of information about language models for each of these four aspects. We conduct a detailed analysis of existing solutions, put forth a number of recommendations, and highlight directions for future advancements. On the other hand, it serves as a synthesized overview of the licences and capabilities of the most popular LMaaS.

Decentralised social media platforms are increasingly being recognised as viable alternatives to their centralised counterparts. Among these, Mastodon stands out as a popular alternative, offering a citizen-powered option distinct from larger and centralised platforms like Twitter/X. However, the future path of Mastodon remains uncertain, particularly in terms of its challenges and the long-term viability of a more citizen-powered internet. In this paper, following a pre-study survey, we conducted semi-structured interviews with 16 Mastodon instance administrators, including those who host instances to support marginalised and stigmatised communities, to understand their motivations and lived experiences of running decentralised social media. Our research indicates that while decentralised social media offers significant potential in supporting the safety, identity and privacy needs of marginalised and stigmatised communities, they also face considerable challenges in content moderation, community building and governance. We emphasise the importance of considering the community's values and diversity when designing future support mechanisms.

In recent years, growing research has been made on supporting children to become more autonomous in the digital environment around them. However, there has been little consensus regarding the conceptualisation of digital autonomy for children in the HCI community and how best they can be supported. Through a systematic review of autonomy-supportive designs within HCI research, this paper makes three contributions: a landscape overview of the existing conceptualisation of Digital Autonomy for children within HCI; a framework of 12 distinct design mechanisms for supporting children’s digital autonomy, clustered into 5 categories by their common mechanisms; and an identification of 5 critical design considerations for future support of children’s digital autonomy. Our findings provide a critical understanding of current support for children’s digital autonomy in HCI. We highlight the importance of considering children’s digital autonomy from multi-perspectives and suggest critical factors and gaps to be considered for future autonomy-supportive designs.

Information asymmetries create extractive, often harmful relationships between platform workers (e.g., Uber or Deliveroo drivers) and their algorithmic managers. Recent HCI studies have put forward more equitable platform designs but leave open questions about the social and technical infrastructures required to support them without the cooperation of platforms. We conducted a participatory design study in which platform workers deconstructed and re-imagined Uber’s schema for driver data. We analyzed the data structures and social institutions participants proposed, focusing on the stakeholders, roles, and strategies for mitigating conflicting interests of privacy, personal agency, and utility. Using critical theory, we reflected on the capability of participatory design to generate bottom-up collective data infrastructures. Based on the plurality of alternative institutions participants produced and their aptitude to navigate data stewardship decisions, we propose user-configurable tools for lightweight data institution building, as an alternative to redesigning existing platforms or delegating control to centralized trusts.

Datafication refers to the practices through which children’s online actions are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in ways including behavioural engineering and monetisation. Previous research has shown that not only do children care significantly about various aspects of datafication, but they demand a chance to take action. Through 10 co-design sessions with 53 children, we examined how children in the UK want to be supported to cope with the datafication practices. Our findings provide insights for creating age-appropriate support for children’s algorithmic literacy development, highlighting and unpacking the importance of no one-size-fitting-all designs to support children’s coping with datafication. We contribute a first understanding of how children aged 7–14 would like to be supported with datafication and what future data-driven digital experiences should be like for them, who demand a shift of the current data ecosystem towards a more humane-by-design and autonomy-supportive future.

AI systems are becoming increasingly pervasive within children’s devices, apps, and services. However, it is not yet well-understood how risks and ethical considerations of AI relate to children. This paper makes three contributions to this area: first, it identifies ten areas of alignment between general AI frameworks and codes for age-appropriate design for children. Then, to understand how such principles relate to real application contexts, we conducted a landscape analysis of children’s AI systems, via a systematic literature review including 188 papers. This analysis revealed a wide assortment of applications, and that most systems’ designs addressed only a small subset of principles among those we identified. Finally, we synthesised our findings in a framework to inform a new “Code for Age-Appropriate AI”, which aims to provide timely input to emerging policies and standards, and inspire increased interactions between the AI and child-computer interaction communities.

Digital harms are widespread in the mobile ecosystem. As these devices gain ever more prominence in our daily lives, so too increases the potential for malicious attacks against individuals. The last line of defense against a range of digital harms – including digital distraction, political polarisation through hate speech, and children being exposed to damaging material – is the user interface. This work introduces GreaseTerminator to enable researchers to develop, deploy, and test interventions against these harms with end-users. We demonstrate the ease of intervention development and deployment, as well as the broad range of harms potentially covered with GreaseTerminator in five in-depth case studies.

Much effort has been invested in designing digital systems that keep people ‘hooked’. By contrast, comparatively little is known about how designers can support people in re-gaining control. Online, however, hundreds of apps and browser extensions promise to help people self-regulate use of digital devices. Reviews and popularity metrics for these digital self-control tools (DSCTs) can indicate which design patterns are useful in the wild. Moreover, they reveal how platforms like Android and iOS differ in the ecosystems they enable for DSCTs, which has important implications for end users. We analysed reviews, installation numbers, and ratings for 334 DSCTs on the Google Play, Chrome Web, and Apple App stores, investigating what user reviews reveal about usage contexts and key design challenges, and how functionality relates to popularity metrics. Our thematic analysis of 1,529 reviews (sampled from a data set of 53,978 distinct reviews scraped in March 2019) found that DSCTs are seen as highly important for focusing on less instantly rewarding tasks when digital distractions are easily available. Users seek DSCTs that adapt to their personal definitions of distraction, and provide support that is sufficient to change behaviour without feeling too coercive. Reviewers suggested combining design patterns to provide a level of support that is ‘just right’. This was mirrored in the ratings, where tools that combine different types of design patterns (e.g., website blocking and goal reminders) tended to receive higher ratings than those implementing a single type. We discuss implications for research and design, including how design patterns in DSCTs interact, and how psychological reactance to DSCTs can be reduced.

Datafication, which is the process in which children's actions online are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in multiple ways that include behavioural engineering, and monetisation, is becoming increasing common in the online world today. However, we know little about how children feel about such practices and how they perceive datafication. Through online interviews with 48 children aged 7-13 from UK schools, we examined how children perceive datafication practices, especially how such practices could make inference on them. We identified three key knowledge gaps in children's perceptions, including their lack of recognition of who were involved in the data processing and how, data being transmitted across platforms, and their data ownership. Through situating our findings under a critical algorithmic literacy framework, our findings provided some immediate indications regarding how we could better support children in the datafied society through more transparency and autonomy-supportive designs, as well as the need for a fundamental shift of the current data governance structure.

Abstract While many studies have looked at privacy properties of the Android and Google Play app ecosystem, comparatively much less is known about iOS and the Apple App Store, the most widely used ecosystem in the US. At the same time, there is increasing competition around privacy between these smartphone operating system providers. In this paper, we present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. We find that third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at children. In the children’s category, iOS apps tended to use fewer advertising-related tracking than their Android counterparts, but could more often access children’s location. Across all studied apps, our study highlights widespread potential violations of US, EU and UK privacy law, including 1) the use of third-party tracking without user consent, 2) the lack of parental consent before sharing personally identifiable information (PII) with third-parties in children’s apps, 3) the non-data-minimising configuration of tracking libraries, 4) the sending of personal data to countries without an adequate level of data protection, and 5) the continued absence of transparency around tracking, partly due to design decisions by Apple and Google. Overall, we find that neither platform is clearly better than the other for privacy across the dimensions we studied.

Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of computational analysis. Due to their relative novelty, complexity, and opacity, these technologies provoke a variety of novel questions for design and governance. We interviewed researchers, developers, industry leaders, policymakers, and designers involved in their deployment to explore motivations, expectations, perceived opportunities and barriers to adoption. This provided insight into several pertinent challenges facing the adoption of these technologies, including: how they might make a nebulous concept like privacy computationally tractable; how to make them more usable by developers; and how they could be explained and made accountable to stakeholders and wider society. We conclude with implications for the development, deployment, and responsible governance of these privacy-preserving computation techniques.

Parental control apps, which are mobile apps that allow parents to monitor and restrict their children's activities online, are becoming increasingly adopted by parents as a means of safeguarding their children's online safety. However, it is not clear whether these apps are always beneficial or effective in what they aim to do; for instance, the overuse of restriction and surveillance has been found to undermine parent-child relationship and children's sense of autonomy. While previous research has categorised and taken inventory of key features of popular parental control apps, they have not systematically analysed the ways such features were designed or realised in such apps, or in particular how aspects of such designs might relate to parents and children's experiences with such apps. In this work, we investigate this gap, asking specifically: how might children's and parents' perceptions be related to how parental control features were designed? To investigate this question, we conducted an analysis of 58 top Android parental control apps designed for the purpose of promoting children's online safety, finding three major axes of variation in how key restriction and monitoring features were realised: granularity, feedback/transparency, and parent-child communications support. To relate these axes to perceived benefits and problems, we then analysed 3264 app reviews to identify references to aspects of the each of the axes above, to understand children's and parents' views of how such dimensions related to their experiences with these apps. Our findings led towards 1) an understanding of how parental control apps realise their functionalities differently along three axes of variation, 2) an analysis of exactly the ways that such variation influences children's and parents' perceptions, respectively of the usefulness or effectiveness of these apps, and finally 3) an identification of design recommendations and opportunities for future apps by contextualising our findings within existing digital parenting theories.

The increasingly widespread use of 'smart' devices has raised multifarious ethical concerns regarding their use in domestic spaces. Previous work examining such ethical dimensions has typically either involved empirical studies of concerns raised by specific devices and use contexts, or alternatively expounded on abstract concepts like autonomy, privacy or trust in relation to 'smart homes' in general. This paper attempts to bridge these approaches by asking what features of smart devices users consider as rendering them 'smart' and how these relate to ethical concerns. Through a multimethod investigation including surveys with smart device users (n=120) and semi-structured interviews (n=15), we identify and describe eight types of smartness and explore how they engender a variety of ethical concerns including privacy, autonomy, and disruption of the social order. We argue that this middle ground, between concerns arising from particular devices and more abstract ethical concepts, can better anticipate potential ethical concerns regarding smart devices.

Beyond being the world's largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing. To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys. Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of 'distraction'.

Today, the Web connects over half the world's population, many of whom use it to stay connected to a multiplicity of vital digital public and private services, impacting every aspect of their lives. Access to the Web and underlying Internet is seen as essential for all—even a fundamental human right [7]. However, many contend that the power structure on large swaths of the Web has become inverted; they argue that instead of being run for and by users, it has been made to serve the platforms themselves, and the powerful actors that sponsor such platforms to run targeted advertising on their behalf. In such an ad-driven platform ecosystem, users, including their beliefs, data, and attention, have become traded commodities [13].

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.

Children under 11 are often regarded as too young to comprehend the implications of online privacy. Perhaps as a result, little research has focused on younger kids' risk recognition and coping. Such knowledge is, however, critical for designing efficient safeguarding mechanisms for this age group. Through 12 focus group studies with 29 children aged 6-10 from UK schools, we examined how children described privacy risks related to their use of tablet computers and what information was used by them to identify threats. We found that children could identify and articulate certain privacy risks well, such as information oversharing or revealing real identities online; however, they had less awareness with respect to other risks, such as online tracking or game promotions. Our findings offer promising directions for supporting children's awareness of cyber risks and the ability to protect themselves online.

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

Most smartphone apps collect and share information with various first and third parties; yet, such data collection practices remain largely unbeknownst to, and outside the control of, end-users. In this paper, we seek to understand the potential for tools to help people refine their exposure to third parties, resulting from their app usage. We designed an interactive, focus-plus-context display called X-Ray Refine (Refine) that uses models of over 1 million Android apps to visualise a person's exposure profile based on their durations of app use. To support exploration of mitigation strategies, emphRefine can simulate actions such as app usage reduction, removal, and substitution. A lab study of emphRefine found participants achieved a high-level understanding of their exposure, and identified data collection behaviours that violated both their expectations and privacy preferences. Participants also devised bespoke strategies to achieve privacy goals, identifying the key barriers to achieving them.

Patient-generated data, such as data from wearable fitness trackers and smartphone apps, are viewed as a valuable information source towards personalised healthcare. However, studies in specific clinical settings have revealed diverse barriers to their effective use. In this paper, we address the following question: are there barriers prevalent across distinct workflows in clinical settings to using patient-generated data? We conducted a two-part investigation: a literature review of studies identifying such barriers; and interviews with clinical specialists across multiple roles, including emergency care, cardiology, mental health, and general practice. We identify common barriers in a six-stage workflow model of aligning patient and clinician objectives, judging data quality, evaluating data utility, rearranging data into a clinical format, interpreting data, and deciding on a plan or action. This workflow establishes common ground for HCI practitioners and researchers to explore solutions to improving the use of patient-generated data in clinical practices.

Third-party networks collect vast amounts of data about users via websites and mobile applications. Consolidations among tracker companies can significantly increase their individual tracking capabilities, prompting scrutiny by competition regulators. Traditional measures of market share, based on revenue or sales, fail to represent the tracking capability of a tracker, especially if it spans both web and mobile. This article proposes a new approach to measure the concentration of tracking capability, based on the reach of a tracker on popular websites and apps. Our results reveal that tracker prominence and parent–subsidiary relationships have significant impact on accurately measuring concentration.

Most users of smartphone apps remain unaware of what data about them is being collected, by whom, and how these data are being used. In this mixed methods investigation, we examine the question of whether revealing key data collection practices of smartphone apps may help people make more informed privacy-related decisions. To investigate this question, we designed and prototyped a new class of privacy indicators, called Data Controller Indicators (DCIs), that expose previously hidden information flows out of the apps. Our lab study of DCIs suggests that such indicators do support people in making more confident and consistent choices, informed by a more diverse range of factors, including the number and nature of third-party companies that access users' data. Furthermore, personalised DCIs, which are contextualised against the other apps an individual already uses, enable them to reason effectively about the differential impacts on their overall information exposure.

Deception is typically regarded as a morally impoverished choice. However, in the context of increasingly intimate, connected and ramified systems of online interaction, manipulating information in ways that could be considered deceptive is often necessary, useful, and even morally justifiable. In this study, we apply a speculative design approach to explore the idea of tools that assist in pro-social forms of online deception, such as those that conceal, distort, falsify and omit information in ways that promote sociality. In one-on-one semi-structured interviews, we asked 15 participants to respond to a selection of speculations, consisting of imagined tools that reify particular approaches to deception. Participants reflected upon potential practical, ethical, and social implications of the use of such tools, revealing a variety of ways such tools might one day encourage polite behaviour, support individual autonomy, provide a defence against privacy intrusions, navigate social status asymmetries, and even promote more open, honest behaviour.

While the Quantified Self and personal informatics fields have focused on the individual's use of self-logged data about themselves, the same kinds of data could, in theory, be used to improve diagnosis and care planning. In this paper, we seek to understand both the opportunities and bottlenecks in the use of self-logged data for differential diagnosis and care planning during patient visits to both primary and secondary care. We first conducted a literature review to identify potential factors influencing the use of self-logged data in clinical settings. This informed the design of our experiment, in which we applied a vignette-based role-play approach with general practitioners and hospital specialists in the US and UK, to elicit reflections on and insights about using patient self-logged data. Our analysis reveals multiple opportunities for the use of self-logged data in the differential diagnosis workflow, identifying capture, representational, and interpretational challenges that are potentially preventing self-logged data from being effectively interpreted and applied by clinicians to derive a patient's prognosis and plan of care.

In this paper, we investigate a method for constructing cascades of information co-occurrence, which is suitable to trace emergent structures in information in scenarios where rich contextual features are unavailable. Our method relies only on the temporal order of content-sharing activities, and intrinsic properties of the shared content itself. We apply this method to analyse information dissemination patterns across the active online citizen science project Planet Hunters, a part of the Zooniverse platform. Our results lend insight into both structural and informational properties of different types of identifiers that can be used and combined to construct cascades. In particular, significant differences are found in the structural properties of information cascades when hashtags as used as cascade identifiers, compared with other content features. We also explain apparent local information losses in cascades in terms of information obsolescence and cascade divergence; e.g., when a cascade branches into multiple, divergent cascades with combined capacity equal to the original.

Designing an effective and sustainable citizen science (CS)project requires consideration of a great number of factors. This makes the overall process unpredictable, even when a sound, user-centred design approach is followed by an experienced team of UX designers. Moreover, when such systems are deployed, the complexity of the resulting interactions challenges any attempt to generalisation from retrospective analysis. In this paper, we present a case study of the largest single platform of citizen driven data analysis projects to date, the Zooniverse. By eliciting, through structured reflection, experiences of core members of its design team, our grounded analysis yielded four sets of themes, focusing on Task Specificity, Community Development, Task Design and Public Relations and Engagement, supported by two-to-four specific design claims each. For each, we propose a set of design claims (DCs), drawing comparisons to the literature on crowdsourcing and online communities to contextualise our findings.

Crowdsourcing via paid microtasks has been successfully applied in a plethora of domains and tasks. Previous efforts for making such crowdsourcing more effective have considered aspects as diverse as task and workflow design, spam detection, quality control, and pricing models. Our work expands upon such efforts by examining the potential of adding gamification to microtask interfaces as a means of improving both worker engagement and effectiveness. We run a series of experiments in image labeling, one of the most common use cases for microtask crowdsourcing, and analyse worker behavior in terms of number of images completed, quality of annotations compared against a gold standard, and response to financial and game-specific rewards. Each experiment studies these parameters in two settings: one based on a state-of-the-art, non-gamified task on CrowdFlower and another one using an alternative interface incorporating several game elements. Our findings show that gamification leads to better accuracy and lower costs than conventional approaches that use only monetary incentives. In addition, it seems to make paid microtask work more rewarding and engaging, especially when sociality features are introduced. Following these initial insights, we define a predictive model for estimating the most appropriate incentives for individual workers, based on their previous contributions. This allows us to build a personalised game experience, with gains seen on the volume and quality of work completed.

This article provides a technical review of semantic search methods used to support text‐based search over formal Semantic Web knowledge bases. Our focus is on ranking methods and auxiliary processes explored by existing semantic search systems, outlined within broad areas of classification. We present reflective examples from the literature in some detail, which should appeal to readers interested in a deeper perspective on the various methods and systems implemented in the outlined literature. The presentation covers graph exploration and propagation methods, adaptations of classic probabilistic retrieval models, and query‐independent link analysis via flexible extensions to the PageRank algorithm. Future research directions are discussed, including development of more cohesive retrieval models to unlock further potentials and uses, data indexing schemes, integration with user interfaces, and building community consensus for more systematic evaluation and gradual development.

The information processing capabilities of humans enable them to opportunistically draw and integrate knowledge from nearly any information source. However, the integration of digital, structured data from diverse sources remains difficult, due to problems of heterogeneity that arise when data modelled separately are brought together. In this paper, we present an investigation of the feasibility of extending Personal Information Management (PIM) tools to support lightweight, user-driven mixing of previously un-integrated data, with the objective of allowing users to take advantage of the emerging ecosystems of structured data currently becoming available. In this study, we conducted an exploratory, sequential, mixed-method investigation, starting with two pre-studies of the data integration needs and challenges, respectively, of Web-based data sources. Observations from these pre-studies led to DataPalette, an interface that introduced simple co-reference and group multi-path-selection mechanisms for working with terminologically and structurally heterogeneous data. Our lab study showed that participants readily understood the new interaction mechanisms which were introduced. Participants made more carefully justified decisions, even while weighing a greater number of factors, moreover expending less effort, during subjective-choice tasks when using DataPalette, than with a control set-up.

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend.

Research in the Semantic Web, especially in modeling virtual communities, has provided models useful to represent the richness of these social network interactions. The SIOC (Semantically-Interlinked Online Communities) vocabulary provides concepts a

Over the last decade, a sophisticated underground economy has emerged over the Internet in which cybercriminals collaborate and trade different goods and services. This study takes a unique approach towards understanding the functioning of the underground economy by focusing on the social dynamics between the cybercriminals. Using anonymized private messaging records from four underground forums formerly operating as online black markets, this study aims to examine the structural properties of the networks of personal interactions between the cybercriminals and to turn the findings into actionable intelligence for tackling the problem of profit-driven cybercrime.

The World Wide Web serves as a valuable source of culture-relevant information, which can be used to support cultural modeling and analysis activities. Part of the challenge in exploiting the Web as a source of culture-relevant information relates to the need to detect and extract information about beliefs, attitudes, and values from a variety of different resources. The Web, thus, features a rich variety of information resources, and these are seldom categorized with respect to the dimensions in which cultural analysts are interested. Exploiting the Web as a source of culture-relevant information therefore requires techniques and approaches that enable cultural analysts to extract relevant information and organize extracted content in various ways. In this paper, we outline an approach to assist cultural analysts in the extraction and organization of relevant information. We show techniques that can be used to extract information of the attitudes, beliefs, and values of individuals, and how this data can, in turn, be used to support cultural modeling and analysis.

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high‐quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph‐based algorithm, SPEAR (spamming‐resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext‐induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

This paper describes an Open Linked Data backlinking service, a generic architecture component to support the discovery of useful links between items across highly connected data sets. Using Public Sector Information (PSI) currently available as Linked Data, we demonstrate that contemporary publishing practices do not adequately support the ability to navigate or automatically traverse between resources published by different vendors, or the capacity to discover information relevant to a particular URL Although some useful services in this area have been developed, such as large triple indexes of published data, and the collection of sameAs relationships between individuals, we believe that an important component is missing: a mechanism to discover the backlinks to relevant resources that cannot be found by direct URI resolution. We present the implementation of such a component, integrating data from various PSI sources.

Collaborative tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in collaborative tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents.

With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.

The current trend in distributed systems is towards service-based integration. This article describes an ontology-driven framework implemented to provide knowledge management for data of different modalities, with multimedia processing, annotation, and reasoning provided by remote services. The framework was developed in, and is presented in the context of, the Medical Imaging and Advanced Knowledge Technologies (MIAKT) project that sought to support the Multidisciplinary Meetings (MDMs) that take place during breast cancer screening for diagnosing the patient. However, the architecture is entirely independent of the specific application domain and can be quickly prototyped into new domains. An Enterprise server provides resource access to a client-side presentation application which, in turn, provides knowledge visualization and markup of any supported media, as defined by a domain-dependent ontology-supported language.

There is a growing realization among many researchers that if we want to model the Web and understand its future trajectory; if we want to understand the architectural principles that have provided for its growth; and if we want to be sure that it supports the basic social values of trustworthiness, privacy, and respect for social boundaries, then we must chart out a research agenda that targets the Web as a primary focus of attention. This is Web Science and this invited talk will discuss the emergence of this exciting new discipline.

Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.

As more ontologies become publicly available, finding the "right" ontologies becomes much harder. In this paper, we address the problem of ontology search: finding a collection of ontologies from an ontology repository that are relevant to the user's query. In particular, we look at the case when users search for ontologies relevant to a particular topic (e.g., an ontology about anatomy). Ontologies that are most relevant to such query often do not have the query term in the names of their concepts (e.g., the Foundational Model of Anatomy ontology does not have the term "anatomy" in any of its concepts' names). Thus, we present a new ontology-search technique that helps users in these types of searches. When looking for ontologies on a particular topic (e.g., anatomy), we retrieve from the Web a collection of terms that represent the given domain (e.g., terms such as body, brain, skin, etc. for anatomy). We then use these terms to expand the user query. We evaluate our algorithm on queries for topics in the biomedical domain against a repository of biomedical ontologies. We use the results obtained from experts in the biomedical-ontology domain as the gold standard. Our experiments demonstrate that using our method for query expansion improves retrieval results by a 113%, compared to the tools that search only for the user query terms and consider only class and property names (like Swoogle). We show 43% improvement for the case where not only class and property names but also property values are taken into account.

Ontologies, as a knowledge representation paradigm, rely on semantics for performing logical inference. However, in the context of ontology query and reuse, the semantics are hard to be manipulated. When searching for ontologies and identifying potentially relevant candidates, a mechanism is required to exploit (i) the appropriate semantic restrictions as well as the (ii) meaning of concepts/properties. This paper proposes an approach that will address both requirements. The contribution of this paper is to conceptually detail how a set of ontology indices can be obtained. More specifically, to obtain a relevant set of keywords describing an ontology, we use logic-based constructors to generate an initial set of keywords. The meaning of these keywords is further explored by consulting a comprehensive text corpora serving as the background/domain knowledge. The novelty of our work lies in the combination of logic-based formalisms with a collaboratively contributed web repository. We illustrate our work by the means of a real world example.

The issue of improved situation awareness is a key concern for military agencies, promising to deliver strategic advantages in a variety of conflict and non-conflict scenarios. Improved situation awareness can benefit operational effectiveness by facilitating the planning process, improving the quality and timeliness of decisions, and providing better feedback regarding the strategic consequences of military actions. In this paper, we aim to show how a combination of semantic technologies and user interface design initiatives can be used to improve situation awareness in a simulated humanitarian relief scenario. We describe the development of a technical demonstrator system, the AKTiveSA TDS, which integrates a variety of knowledge technologies and visualization components within the context of a unitary application framework. We also describe our approach to scenario development, knowledge acquisition, ontology engineering and system design. Some specific problems encountered during system development are discussed, e.g. the performance overheads associated with rules-based processing, and potential solution strategies for these problems are presented alongside a description of future development activities.

Knowledge has become increasingly important to support intelligent process automation and collaborative problem solving in large-scale science over the Internet. This paper addresses distributed knowledge management, its approach and methodology, in the context of grid application. We start by analyzing the nature of grid computing and its requirements for knowledge support; then, we discuss knowledge characteristics and the challenges for knowledge management on the grid. A semantic Web-based approach is proposed to tackle the six challenges of the knowledge lifecycle - namely, those of acquiring, modeling, retrieving, reusing, publishing, and maintaining knowledge. To facilitate the application of the approach, a systematic methodology is conceived and designed to provide a general implementation guideline. We use a real-world Grid application, the GEODISE project, as a case study in which the core semantic Web technologies such as ontologies, semantic enrichment, and semantic reasoning are used for knowledge engineering and management. The case study has been fully implemented and deployed through which the evaluation and validation for the approach and methodology have been performed

The design and maintenance of complex engineering systems such as a jet engine generates a significant amount of documentation. Increasingly, aerospace manufacturers are shifting their focus from selling products to providing services. As a result, when designing new engines, engineers must increasingly consider the life-cycle requirements in addition to design parameters. To identify possible areas of concern, engineers must obtain knowledge gained from the entire life of an engine. However, because of the size and distributed nature of a company's operation, engineers often do not have access to front-line maintenance data. In addition, the large number of documents accrued makes it impossible to examine thoroughly. This paper presents a prototype knowledge-based document repository for such an application. It searches and analyzes distributed document resources, and provides engineers with a summary view of the underlying knowledge. The aim is to aid engineers in creating design requirement documents that incorporate aftermarket issues. Unlike existing document repositories and digital libraries, our approach is knowledge-based, where users browse summary reports instead of following suggested links. To test the validity of our proposed architecture, we have developed and deployed a working prototype. The prototype has been demonstrated to engineers and received positive reviews.

The World Wide Web has been revolutionary in terms of impact, scale and outreach. At every level society has been changed in some way by the Web. This Panel will consider likely developments in this extraordinary human construct as we attempt to realise the Next Wave of the Web - a Semantic Web.Nigel Shadbolt will Chair a discussion that will focus on the prospects for the Semantic Web, its likely form and the challenges it faces. Can we achieve the necessary agreements on shared meaning for the Semantic Web? Can we achieve a critical mass of semantically annotated data and content? How are we to trust such content? Do the scientific and commercial drivers really demand a Semantic Web? How will the move to a mobile and ubiquitous Web affect the Semantic Web? How does Web 2.0 relate to the Semantic Web?

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e‐Science. The framework uses ontology as the conceptual backbone and adopts the service‐oriented computing paradigm for information‐ and knowledge‐level computation. We further present a semantics‐based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e‐Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology‐enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.

Grid computing offers significant enhancements to our capabilities for computation, information processing, and collaboration, and has exciting ambitions in many fields of endeavor. We argue that the full richness of the Grid vision, with its application in e-Science, e-Research, or e-Business, requires the "Semantic Grid." The Semantic Grid is an extension of the current Grid in which information and services are given well-defined meaning, better enabling computers and people to work in cooperation. To this end, we outline the requirements of the Semantic Grid, discuss the state of the art in achieving them, and identify the key research challenges in realizing this vision.

The future success of Grid-enabled e-Science depends on the availability of semantic/knowledge-rich resources on the Grid, i.e., the so-called semantic Grid. This requires not only novel knowledge modelling and representation formalisms but also a shift of knowledge acquisition and population from a limited number of specialised knowledge engineers to resource providers. To this end we have developed a lightweight ontology-enabled tool, "Function Annotator", to support resource providers in capturing and publishing resource semantics and knowledge. Function Annotator takes a different line to most knowledge acquisition tools in that it is designed for use by resource providers, probably in the absence of a knowledge engineer. Its aim is to facilitate large scale knowledge population on the Grid. Function Annotator is built on the state-of-the-art of semantic web technologies, such as ontologies, OWL, instance store and DL-based reasoning, thus ensuring flexibility and scalability on the Grid. This paper describes the tool's role in a Grid-oriented resource lifecycle, its underlying technologies and implementation. It also illustrates the usage of the tool in the context of engineering design search and optimisation.

We present a Semantic Web application that we callCS AKTive Space. The application exploits a wide range of semantically heterogeneousand distributed content relating to Computer Science research in theUK. This content is gathered on a continuous basis using a variety of methods including harvesting and scraping as well as adopting a range models for content acquisition. The content currently comprises aroundten million RDF triples and we have developed storage, retrieval andmaintenance methods to support its management. The content is mediated through an ontology constructed for the application domainand incorporates components from other published ontologies. CS AKTive Spacesupports the exploration of patterns and implications inherent in the content and exploits a variety of visualisations and multi dimensional representations. Knowledge services supported in the applicationinclude investigating communities of practice: who is working, researching or publishing with whom. This work illustrates a number ofsubstantial challenges for the Semantic Web. These include problems of referential integrity, tractable inference and interaction support. Wereview our approaches to these issues and discuss relevant related work.

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

A wide range of algorithms have been developed for various types of automated negotiation. In developing such algorithms the main focus has been on their efficiency and their effectiveness. However, this is only part of the picture. Agents typically negotiate on behalf of their owners and for this to be effective the agent must be able to adequately represent the owners' preferences. However, the process by which such knowledge is acquired is typically left unspecified. To remove this shortcoming, we present a case study indicating how the knowledge for a particular negotiation algorithm can be acquired. More precisely, according to the analysis on the automated negotiation model, we identified that user trade-off preferences play a fundamental role in negotiation in general. This topic has been addressed little in the research area of user preference elicitation for general decision making problems as well. In a previous paper, we proposed an exhaustive method to acquire user trade-off preferences. In this paper, we developed another method to remove the limitation of the high user workload of the exhaustive method. Although we cannot say that it can exactly capture user trade-off preferences, it models the main commonalities of trade-off relations and reflects users' individualities as well.

The interpretation of medical evidence is normally presented in terms of a controlled, but diversely expressed specialist vocabulary and natural language phrases. Such informally expressed data require human intervention to ascertain its relevance in any specific case. In order to facilitate machine-based reasoning about the evidence gathered, additional interpretive semantics must be attached to the data; a shift from a merely data-intensive approach to a semantics-rich model of evidence. In this paper, we present a system to formally annotate medical images captured to aid the diagnosis and management of breast cancer, that enables a series of semantics-based operations to be performed. Our approach is grounded upon an imaging ontology specifying the domain knowledge and a description logic (DL) taxonomic inferential engine responsible for semantics-based reasoning and image retrieval.

Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a diverse and dynamic environment. Recommender systems help where explicit search queries are not available or are difficult to formulate, learning the type of thing users like over a period of time.We explore an ontological approach to user profiling in the context of a recommender system. Building on previous work involving ontological profile inference and the use of external ontologies to overcome the cold-start problem, we explore the idea of profile visualization to capture further knowledge about user interests. Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers. Both our ontological approach to user profiling and our visualization of user profiles are novel ideas to recommender systems. A year long experiment is conducted with over 200 staff and students at the University of Southampton. The effectiveness of visualizing profiles and eliciting profile feedback is measured, as is the overall effectiveness of the recommender system.

No abstract available.

The Web Services world consists of loosely-coupled distributed systems which adapt to ad-hoc changes by the use of service descriptions that enable opportunistic service discovery. At present, these service descriptions are semantically impoverished, being concerned with describing the functional signature of the services rather than characterising their meaning. In the Semantic Web community, the DAML Services effort attempts to rectify this by providing a more expressive way of describing Web services using ontologies. However, this approach does not separate the domain-neutral communicative intent of a message (considered in terms of speech acts) from its domain-specific content, unlike similar developments from the multi-agent systems community.In this paper, we describe our experiences of designing and building an ontologically motivated Web Services system for situational awareness and information triage in a simulated humanitarian aid scenario. In particular, we discuss the merits of using techniques from the multi-agent systems community for separating the intentional force of messages from their content, and the implementation of these techniques within the DAML Services model.

We are developing a Grid Enabled Optimisation and Design Search system (GEODISE). It offers grid-based access to a state-of-the-art collection of optimisation and design search tools, industrial strength analysis codes, and distributed computing and data resources.

Synaptic normalization is used to enforce competitive dynamics in many models of developmental synaptic plasticity. In linear and semilinear Hebbian models, multiplicative synaptic normalization fails to segregate afferents whose activity patterns are positively correlated. To achieve this, the biologically problematic device of subtractive synaptic normalization must be used instead. Our own model of competition for neurotrophic support, which can segregate positively correlated afferents, was developed in part in an attempt to overcome these problems by removing the need for synaptic normalization altogether. However, we now show that the dynamics of our model decompose into two decoupled subspaces, with competitive dynamics being implemented in one of them through a nonlinear Hebb rule and multiplicative synaptic normalization. This normalization is "emergent" rather than imposed. We argue that these observations permit biologically plausible forms of synaptic normalization to be viewed as abstract and general descriptions of the underlying biology in certain scaleless models of synaptic plasticity.

Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.

We examine the claim that a class of sprouting-and-retraction models is mathematically equivalent to a fixed-anatomy model. We accept, subject to important caveats, a narrow mathematical equivalence of the energy functions in both classes of model. We argue that this narrow equivalence of energy functions does not, however, entail equivalence of the models. Indeed, the claim of complete model equivalence hides significant dynamical differences between the approaches, which we discuss. We also disagree that our work demonstrates that subtractive constraint enforcement is natural in fixed-anatomy models.

Neurotrophic factors, particularly the neurotrophin gene family of neurotrophic factors, are implicated in activity-dependent anatomical plasticity in the visual cortex and at the neuromuscular junction. Accumulating evidence implicates neurotrophic factors as possible mediators of activity-dependent competition between afferents, leading to the segregation of afferents' arbors on the target space. We present a biologically plausible mathematical model of competition for neurotrophic factors. We show that the model leads to anatomical segregation, provided that the levels of neurotrophic factors released in an activity-independent manner, or the levels available by exogenous infusion, are below a critical value, which we derive. Above this critical value, afferent segregation breaks down. We also show that the model segregates afferents even in the presence of very highly correlated patterns of afferent activity. The model is therefore ideally suited for application to the development of ocular dominance columns in the kitten visual cortex.

A constraint-based system, PLS, capable of deriving a technically acceptable three-dimensional layout for a chemical process plant of typical complexity is described. In the application of PLS to an exemplar plant, over five hundred instances of constraints were identified and manipulated.

Article Free Access Share on Knowledge representation for closed loop control Authors: N. R. Shadbolt Univ. of Nottingham, Nottingham, UK Univ. of Nottingham, Nottingham, UKView Profile , B. D. Robinson Univ. of Oxford, Oxford, UK Univ. of Oxford, Oxford, UKView Profile , R. K. Stobart View Profile Authors Info & Claims IEA/AIE '89: Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1June 1989 Pages 296–304https://doi.org/10.1145/66617.66653Online:06 June 1989Publication History 2citation325DownloadsMetricsTotal Citations2Total Downloads325Last 12 Months8Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF