2,486 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.

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    Mobile heritage apps have become one of the most popular means for audience engagement and curation of museum collections and heritage contexts. This raises practical and ethical questions for both researchers and practitioners, such as: what kind of audience engagement can be built using mobile apps? what are the current approaches? how can audience engagement with these experience be evaluated? how can those experiences be made more resilient, and in turn sustainable? In this thesis I explore experience design scholarships together with personal professional insights to analyse digital heritage practices with a view to accelerating thinking about and critique of mobile apps in particular. As a result, the chapters that follow here look at the evolution of digital heritage practices, examining the cultural, societal, and technological contexts in which mobile heritage apps are developed by the creative media industry, the academic institutions, and how these forces are shaping the user experience design methods. Drawing from studies in digital (critical) heritage, Human-Computer Interaction (HCI), and design thinking, this thesis provides a critical analysis of the development and use of mobile practices for the heritage. Furthermore, through an empirical and embedded approach to research, the thesis also presents auto-ethnographic case studies in order to show evidence that mobile experiences conceptualised by more organic design approaches, can result in more resilient and sustainable heritage practices. By doing so, this thesis encourages a renewed understanding of the pivotal role of these practices in the broader sociocultural, political and environmental changes.AHRC REAC

    TANDEM: taming failures in next-generation datacenters with emerging memory

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    The explosive growth of online services, leading to unforeseen scales, has made modern datacenters highly prone to failures. Taming these failures hinges on fast and correct recovery, minimizing service interruptions. Applications, owing to recovery, entail additional measures to maintain a recoverable state of data and computation logic during their failure-free execution. However, these precautionary measures have severe implications on performance, correctness, and programmability, making recovery incredibly challenging to realize in practice. Emerging memory, particularly non-volatile memory (NVM) and disaggregated memory (DM), offers a promising opportunity to achieve fast recovery with maximum performance. However, incorporating these technologies into datacenter architecture presents significant challenges; Their distinct architectural attributes, differing significantly from traditional memory devices, introduce new semantic challenges for implementing recovery, complicating correctness and programmability. Can emerging memory enable fast, performant, and correct recovery in the datacenter? This thesis aims to answer this question while addressing the associated challenges. When architecting datacenters with emerging memory, system architects face four key challenges: (1) how to guarantee correct semantics; (2) how to efficiently enforce correctness with optimal performance; (3) how to validate end-to-end correctness including recovery; and (4) how to preserve programmer productivity (Programmability). This thesis aims to address these challenges through the following approaches: (a) defining precise consistency models that formally specify correct end-to-end semantics in the presence of failures (consistency models also play a crucial role in programmability); (b) developing new low-level mechanisms to efficiently enforce the prescribed models given the capabilities of emerging memory; and (c) creating robust testing frameworks to validate end-to-end correctness and recovery. We start our exploration with non-volatile memory (NVM), which offers fast persistence capabilities directly accessible through the processor’s load-store (memory) interface. Notably, these capabilities can be leveraged to enable fast recovery for Log-Free Data Structures (LFDs) while maximizing performance. However, due to the complexity of modern cache hierarchies, data hardly persist in any specific order, jeop- ardizing recovery and correctness. Therefore, recovery needs primitives that explicitly control the order of updates to NVM (known as persistency models). We outline the precise specification of a novel persistency model – Release Persistency (RP) – that provides a consistency guarantee for LFDs on what remains in non-volatile memory upon failure. To efficiently enforce RP, we propose a novel microarchitecture mechanism, lazy release persistence (LRP). Using standard LFDs benchmarks, we show that LRP achieves fast recovery while incurring minimal overhead on performance. We continue our discussion with memory disaggregation which decouples memory from traditional monolithic servers, offering a promising pathway for achieving very high availability in replicated in-memory data stores. Achieving such availability hinges on transaction protocols that can efficiently handle recovery in this setting, where compute and memory are independent. However, there is a challenge: disaggregated memory (DM) fails to work with RPC-style protocols, mandating one-sided transaction protocols. Exacerbating the problem, one-sided transactions expose critical low-level ordering to architects, posing a threat to correctness. We present a highly available transaction protocol, Pandora, that is specifically designed to achieve fast recovery in disaggregated key-value stores (DKVSes). Pandora is the first one-sided transactional protocol that ensures correct, non-blocking, and fast recovery in DKVS. Our experimental implementation artifacts demonstrate that Pandora achieves fast recovery and high availability while causing minimal disruption to services. Finally, we introduce a novel target litmus-testing framework – DART – to validate the end-to-end correctness of transactional protocols with recovery. Using DART’s target testing capabilities, we have found several critical bugs in Pandora, highlighting the need for robust end-to-end testing methods in the design loop to iteratively fix correctness bugs. Crucially, DART is lightweight and black-box, thereby eliminating any intervention from the programmers

    Undergraduate Catalog of Studies, 2023-2024

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    InversOS: Efficient Control-Flow Protection for AArch64 Applications with Privilege Inversion

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    With the increasing popularity of AArch64 processors in general-purpose computing, securing software running on AArch64 systems against control-flow hijacking attacks has become a critical part toward secure computation. Shadow stacks keep shadow copies of function return addresses and, when protected from illegal modifications and coupled with forward-edge control-flow integrity, form an effective and proven defense against such attacks. However, AArch64 lacks native support for write-protected shadow stacks, while software alternatives either incur prohibitive performance overhead or provide weak security guarantees. We present InversOS, the first hardware-assisted write-protected shadow stacks for AArch64 user-space applications, utilizing commonly available features of AArch64 to achieve efficient intra-address space isolation (called Privilege Inversion) required to protect shadow stacks. Privilege Inversion adopts unconventional design choices that run protected applications in the kernel mode and mark operating system (OS) kernel memory as user-accessible; InversOS therefore uses a novel combination of OS kernel modifications, compiler transformations, and another AArch64 feature to ensure the safety of doing so and to support legacy applications. We show that InversOS is secure by design, effective against various control-flow hijacking attacks, and performant on selected benchmarks and applications (incurring overhead of 7.0% on LMBench, 7.1% on SPEC CPU 2017, and 3.0% on Nginx web server).Comment: 18 pages, 9 figures, 4 table

    A novel evaluation framework for recommender systems in big data environments

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    Henriques, R., & Pinto, L. (2023). A novel evaluation framework for recommender systems in big data environments. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120659---We gratefully acknowledge the support of Aptoide in providing access to the data which made this project possible. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Recommender systems were first introduced to solve information overload problems in enterprises. Over the last few decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media, and mobile app stores. Several methods have been proposed over the years to build recommender systems. However, very little work has been done in recommender system evaluation metrics. The most common approach to measuring recommender system’s performance in offline settings is to employ micro or macro averaged versions of standard machine-learning measures. Profit or other business-oriented metrics have been proposed for other predictive analytics problems, such as churn prediction. However, no such metrics have emerged for the recommender system context. In this work, we propose a novel evaluation metric that incorporates information from the online-platform userbase’s behavior. This metric’s rationale is that the recommender system ought to improve customers’ repeatead use of an online platform beyond the baseline level (i.e. in the absence of a recommender system). An empirical application of this novel metric is also presented in a real-world mobile app store, which integrates the dynamics of large-scale big data environments, which are common deployment scenarios for these types of recommender systems. The resulting profit metric is shown to correlate with the existing metrics while also being capable of integrating cost information, thereby providing an additional business benefit context, which allows us to differentiate between two similarly performing models.publishersversionepub_ahead_of_prin

    To make the dominoes fall: A relational-processual approach to societal accountability in the Italian and Spanish anti-corruption arenas

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    In che modo le organizzazioni della società civile (OSC) contribuiscono alla lotta contro la corruzione? Come possono responsabilizzare i rappresentanti politici? La presente tesi si propone di rispondere a queste a queste domande di ricerca, unendo gli studi sulla lotta alla corruzione a quelli sui movimenti sociali e concentrandosi sul concetto di societal accountability, cioè sui meccanismi di controllo e di sanzione dei rappresentanti pubblici. Negli ultimi anni, gli studiosi della corruzione hanno enfatizzato sempre più il ruolo della società civile come antidoto contro la corruzione, a complemento dei meccanismi di accountability statali ed elettorali. Tuttavia, gli studi empirici sugli effetti anticorruzione degli interventi civici non hanno ancora prodotto risultati coerenti. Questo non dovrebbe sorprendere. Se misurare la corruzione è un compito arduo, valutare se e quanto gli scambi corruttivi vengano impediti grazie alle iniziative della società civile sembra virtualmente impossibile. Per questo motivo, il presente lavoro fa un passo indietro e problematizza lo studio della societal accountability, affrontandola non come un insieme predefinito di meccanismi o pratiche messe in atto da attori civici anticorruzione, ma come il risultato di interazioni sostenute e conflittuali tra più attori, civici e non. Per fare ciò, lo studio si ispira alle teorie dei movimenti sociali e concettualizza la societal accountability come un insieme di conseguenze dell’azione collettiva. Pertanto, questo lavoro mira a capire come e in quali condizioni le iniziative anticorruzione dal basso raggiungano risultati di accountability, quali il passaggio di nuove norme, il miglioramento dell’answerability istituzionale e potenziale sanzionatorio. Con questo obiettivo, la tesi si basa sulle evidenze esistenti negli studi sulla corruzione e sull'accountability e contribuisce ai dibattiti in corso sulle conseguenze dell'azione collettiva. Il quadro teorico si concentra sul concetto di influenza, aderendo a un approccio processuale-relazionale. L'influenza è intesa come un'istanza di causalità relazionale, una forma di potere posizionale che consente a più attori di esercitare un controllo sulle conseguenze dell’azione collettiva. Facendo da ponte tra l'approccio strategico-interazionale e i modelli di mediazione, l'analisi chiarisce le strategie seguite dalle OSC nella ricerca di posizioni di influenza, così come i meccanismi attraverso i quali i modelli relazionali producono cambiamento sociale. Il quadro analitico è applicato alle arene anticorruzione in Italia e in Spagna e si restringe a tre specifiche aree di intervento: l'introduzione di leggi sulla trasparenza, l'approvazione di leggi per la protezione dei whistleblower e lo sviluppo di progetti di monitoraggio civico. Il materiale empirico comprende 37 interviste qualitative semi-strutturate, documenti e dati network. Nel complesso, le evidenze raccolte contribuiscono alla letteratura sulla lotta alla corruzione, dimostrando che le OSC contribuiscono, direttamente e indirettamente, alla lotta contro la corruzione ottenendo cambiamenti nelle politiche, aumentando l’answerability del sistema e innescando sanzioni formali e informali quando necessario. Tuttavia, l’analisi comparata dei casi italiano e spagnolo evidenziano differenze rilevanti. In particolare, l'indagine empirica contribuisce agli attuali dibattiti sullo studio della società della social accountability, dimostrando che l'integrazione con le élite politiche può aumentare la probabilità di ottenere di ottenere un cambiamento delle politiche, mentre l'integrazione orizzontale tra gli attori civici può aumentare il loro potenziale sanzionatorio. In definitiva, questo lavoro dimostra come gli approcci processuali-relazionali possano integrare modelli strategici e di mediazione per comprendere meglio il modo in cui gli attori collettivi influenzano il cambiamento politico e sociale. Le osservazioni conclusive sostengono che le interazioni e le relazioni costruite dagli attori nel corso del tempo e in diverse arene fungono da canali di mediazione a livello micro, meso e macro. Complessivamente, ciò dimostra che i singoli attori, i modelli di relazione nelle e tra le arene e le idee sulle relazioni mediano tra le strategie dei attori collettivi, aumentando o limitando così la loro influenza sulla lotta alla corruzione.How do civil society organizations (CSOs) contribute to the struggle against public corruption? How can they hold their political representatives accountable? This thesis aims to answer these wide-ranging research questions, bridging anti-corruption and social movement studies by focusing on societal accountability, i.e., grassroots mechanisms for controlling and sanctioning powerholders. Over the last few years, corruption scholars have increasingly emphasized the role of civil society as an antidote against corruption, complementing state and electoral accountability mechanisms. However, empirical studies on the anti-corruption effects of civic interventions have yet to yield consistent results. This should hardly come as a surprise. If measuring corruption is a challenging task, assessing the extent to which corrupt deals are prevented due to civil society initiatives appears virtually impossible. Hence, this work takes a step back and problematizes the study of societal accountability, approaching it not as a pre-given set of mechanisms or practices deployed by anti-corruption civic actors but as the result of sustained and contentious interactions between multiple players. To do so, the study draws on social movement theories and conceptualizes societal accountability as a set of consequences of collective action efforts. Therefore, this work aims to understand how and under what conditions bottom-up anti-corruption initiatives achieve accountability results such as legal claim attainments, answerability, and sanctioning potential. With this goal in mind, the thesis builds upon existing evidence from corruption and accountability studies and contributes to ongoing debates on the consequences of collective action. The theoretical framework focuses on the concept of influence, subscribing to a processual-relational approach. It understands influence as a relationally emergent instance of causality, a form of positional power that enables multiple players to exert control over the consequences of collective struggles. By bridging the strategic-interaction approach and mediation models; the analysis elucidates the strategies followed by CSOs in seeking positions of influence, as well as the mechanisms through which relational patterns produce social change. The analytical framework is applied to the anti-corruption arenas in Italy and Spain and is narrowed down by focusing on three specific campaigns in each country: introducing transparency laws, passing whistleblowers' protection acts, and developing civic monitoring projects. The empirical material comprises 37 semi-structured qualitative interviews, documents, and network data retrieved through Action Organization Analysis. The corpus of data is analyzed by combining thematic analysis, frame analysis, and a theory-building process tracing through a qualitative network approach. Overall, the evidence collected contributes to the literature on anti-corruption, demonstrating that CSOs, directly and indirectly, contribute to the anti-corruption struggle by achieving policy change, increasing the system's answerability, and triggering formal and informal sanctions when necessary. However, the Italian and Spanish cases' comparative accounts highlight relevant differences. In particular, the empirical investigation contributes to current debates on the study of societal accountability, showing that integration with political elites may increase the likelihood of obtaining policy change, whereas horizontal integration among civic actors may enhance their sanctioning potential. Ultimately, this work shows how processual-relational approaches can help integrate strategic and mediation models to understand better how change-oriented collective actors influence political and social change. The concluding remarks maintain that the interactions and relations built by players over time and across different arenas serve as mediation channels at the micro-, meso-, and macro-levels. Overall, this demonstrates that individual players, patterns of relations in and across arenas, and ideas about relationships mediate between players' strategies, resources, or frames and their contextual conditions, thereby increasing or constraining their influence over the anti-corruption struggl

    Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities

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    The communications and collaborations among activities, pro- cesses, or systems, in general, are the base of complex sys- tems defined as distributed systems. Given the increasing complexity of their structure, interactions, and functionali- ties, many research areas are interested in providing mod- elling techniques and verification capabilities to guarantee their correctness and satisfaction of properties. In particular, the formal methods community provides robust verification techniques to prove system properties. However, most ap- proaches rely on manually designed formal models, making the analysis process challenging because it requires an expert in the field. On the other hand, the BPM community pro- vides a widely used graphical notation (i.e., BPMN) to design internal behaviour and interactions of complex distributed systems that can be enhanced with additional features (e.g., privacy technologies). Furthermore, BPM uses process min- ing techniques to automatically discover these models from events observation. However, verifying properties and ex- pected behaviour, especially in collaborations, still needs a solid methodology. This thesis aims at exploiting the features of the formal meth- ods and BPM communities to provide approaches that en- able formal verification over distributed systems. In this con- text, we propose two approaches. The modelling-based ap- proach starts from BPMN models and produces process al- gebra specifications to enable formal verification of system properties, including privacy-related ones. The process mining- based approach starts from logs observations to automati- xv cally generate process algebra specifications to enable veri- fication capabilities
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