Showing papers by "Royal Holloway, University of London published in 2017"

The open innovation research landscape: established perspectives and emerging themes across different levels of analysis

Abstract: This paper provides an overview of the main perspectives and themes emerging in research on open innovation (OI). The paper is the result of a collaborative process among several OI scholars – having a common basis in the recurrent Professional Development Workshop on ‘Researching Open Innovation’ at the Annual Meeting of the Academy of Management. In this paper, we present opportunities for future research on OI, organised at different levels of analysis. We discuss some of the contingencies at these different levels, and argue that future research needs to study OI – originally an organisational-level phenomenon – across multiple levels of analysis. While our integrative framework allows comparing, contrasting and integrating various perspectives at different levels of analysis, further theorising will be needed to advance OI research. On this basis, we propose some new research categories as well as questions for future research – particularly those that span across research domains that have so far developed in isolation.

Understanding the relationships between tourists' emotional experiences, perceived overall image, satisfaction, and intention to recommend

Abstract: The purpose of this study is to empirically test an integrative model linking tourists’ emotional experiences, perceived overall image, satisfaction, and intention to recommend. The model was tested using data collected from domestic tourists visiting Sardinia, Italy. Results show that tourists’ emotional experiences act as antecedents of perceived overall image and satisfaction evaluations. In addition, overall image has a positive influence on tourist satisfaction and intention to recommend. The study expands current theorizations by examining the merits of emotions in tourist behavior models. From a practical perspective, the study offers important implications for destination marketers.

Performance of the ATLAS trigger system in 2015

Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...

Achieving the United Nations Sustainable Development Goals: An enabling role for accounting research

Abstract: Purpose The purpose of this paper is to establish and advance the role of academic accounting in the pursuit of the United Nations Sustainable Development Goals (SDGs), which are regarded as the most salient point of departure for understanding and achieving environmental and human development ambitions up to (and no doubt beyond) the year 2030. Design/methodology/approach This paper provides a synthesis of interdisciplinary perspectives on sustainable development and integration of this with the accounting for sustainability literature. In addition, potential accounting research contributions are proposed so as to support the development of new research avenues. Findings Existing research in accounting that is relevant to individual SDGs serves as an initial link between them and the accounting discipline. At the same time, the SDGs focus highlights new sites for empirical work (including interdisciplinary investigations) as well as inviting innovation in accounting theoretical frameworks. Moreover, the SDGs provide a context for (re)invigorating accounting’s contribution to sustainable development debates. Originality/value This is the first paper to explore the roles academic accounting can play in furthering achievement of the SDGs through enhanced understanding, critiquing and advancing of accounting policy, practice and theorizing. It is also the first paper to propose a research agenda in this area.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

Abstract: The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Search for new high-mass phenomena in the dilepton final state using 36 fb−1 of proton-proton collision data at √s=13 TeV with the ATLAS detector

Abstract: A search is conducted for new resonant and non-resonant high-mass phenomena in dielectron and dimuon fi nal states. The search uses 36 : 1 fb(-1) of proton-proton collision data, collected at root ...

The Evolution of Android Malware and Android Analysis Techniques

Abstract: With the integration of mobile devices into daily life, smartphones are privy to increasing amounts of sensitive information. Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. This article presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware. This article categorizes systems by methodology and date to evaluate progression and weaknesses. This article also discusses evaluations of industry solutions, malware statistics, and malware evasion techniques and concludes by supporting future research paths.

Tree Diversity Drives Forest Stand Resistance to Natural Disturbances

Abstract: Forests are frequently exposed to natural disturbances, which are likely to increase with global change, and may jeopardize the delivery of ecosystem services. Mixed-species forests have often been shown to be more productive than monocultures, but it is unclear whether this results from mixed stands being in part more resistant to various biotic and abiotic disturbance factors. This review investigates the relationships between tree diversity and stand resistance to natural disturbances and explores the ecological mechanisms behind the observed relationships. Mixed forests appear to be more resistant than monocultures to small mammalian herbivores, soil-borne fungal diseases and specialized insect herbivores. Admixing broadleaves to conifers also increases the resistance to fire and windstorms when compared to pure conifer stands. However, mixed forests may be more affected by drought depending on the species in the mixture. Overall, our findings suggest that mixed forests are more resistant to natural disturbances that are relatively small-scale and selective in their effect. However, benefits provided by mixtures are less evident for larger-scale disturbances. Higher tree diversity translates into increased resistance to disturbances as a result of ecological trait complementarity among species, reduction of fuel and food resources for herbivores, enhancement of diversion or disruption processes, and multi-trophic interactions such as predation or symbiosis. To promote resistance, the selection of tree species with different functional characteristics appears more important than increasing only the number of species in the stand. Trees with different levels of susceptibility to different hazards should be intermixed in order to reduce the amount of exposed resources and to generate barriers against contagion. However, more research is needed to further improve associational resistance in mixed forests, through a better understanding of the most relevant spatial and temporal scales of species interactions and to optimize the overall provision of ecosystem services.

Mentalizing homeostasis: The social origins of interoceptive inference

Abstract: Is the self already relational in its very bodily foundations? The question of whether our mental life is initially and primarily shaped by embodied dimensions of the individual or by interpersonal relations is debated in many fields, including psychology, philosophy, psychoanalysis, and more recently, cognitive neuroscience. In this interdisciplinary target article, we put forward the radical claim that even some of the most minimal aspects of selfhood, namely the feeling qualities associated with being an embodied subject, are fundamentally shaped by embodied interactions with other people in early infancy and beyond. Such embodied interactions allow the developing organism to mentalize its homeostatic regulation. In other words, embodied interactions contribute directly to the building of mental models of the infant’s physiological states, given the need to maintain such states within a given dynamic range despite internal or external perturbations. Specifically, our position rests on the following thr...

Search for new phenomena in dijet events using 37 fb-1 of pp collision data collected at √s=13 TeV with the ATLAS detector

Abstract: Dijet events are studied in the proton-proton collision dataset recorded at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, corresponding to integrated lumino ...

The multisensory basis of the self: From body to identity to others

Abstract: By grounding the self in the body, experimental psychology has taken the body as the starting point for a science of the self. One fundamental dimension of the bodily self is the sense of body owne...

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Abstract: The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Forward and Backward Private Searchable Encryption from Constrained Cryptographic Primitives

Abstract: Using dynamic Searchable Symmetric Encryption, a user with limited storage resources can securely outsource a database to an untrusted server, in such a way that the database can still be searched and updated efficiently. For these schemes, it would be desirable that updates do not reveal any information a priori about the modifications they carry out, and that deleted results remain inaccessible to the server a posteriori. If the first property, called forward privacy, has been the main motivation of recent works, the second one, backward privacy, has been overlooked. In this paper, we study for the first time the notion of backward privacy for searchable encryption. After giving formal definitions for different flavors of backward privacy, we present several schemes achieving both forward and backward privacy, with various efficiency trade-offs. Our constructions crucially rely on primitives such as constrained pseudo-random functions and puncturable encryption schemes. Using these advanced cryptographic primitives allows for a fine-grained control of the power of the adversary, preventing her from evaluating functions on selected inputs, or decrypting specific ciphertexts. In turn, this high degree of control allows our SSE constructions to achieve the stronger forms of privacy outlined above. As an example, we present a framework to construct forward-private schemes from range-constrained pseudo-random functions. Finally, we provide experimental results for implementations of our schemes, and study their practical efficiency.

The ancestral flower of angiosperms and its early diversification

Abstract: Recent advances in molecular phylogenetics and a series of important palaeobotanical discoveries have revolutionized our understanding of angiosperm diversification. Yet, the origin and early evolution of their most characteristic feature, the flower, remains poorly understood. In particular, the structure of the ancestral flower of all living angiosperms is still uncertain. Here we report model-based reconstructions for ancestral flowers at the deepest nodes in the phylogeny of angiosperms, using the largest data set of floral traits ever assembled. We reconstruct the ancestral angiosperm flower as bisexual and radially symmetric, with more than two whorls of three separate perianth organs each (undifferentiated tepals), more than two whorls of three separate stamens each, and more than five spirally arranged separate carpels. Although uncertainty remains for some of the characters, our reconstruction allows us to propose a new plausible scenario for the early diversification of flowers, leading to new testable hypotheses for future research on angiosperms.

Biodiversity and ecosystem functioning relations in European forests depend on environmental context

Abstract: The importance of biodiversity in supporting ecosystem functioning is generally well accepted. However, most evidence comes from small-scale studies, and scaling-up patterns of biodiversity-ecosystem functioning (B-EF) remains challenging, in part because the importance of environmental factors in shaping B-EF relations is poorly understood. Using a forest research platform in which 26 ecosystem functions were measured along gradients of tree species richness in six regions across Europe, we investigated the extent and the potential drivers of context dependency of B-EF relations. Despite considerable variation in species richness effects across the continent, we found a tendency for stronger B-EF relations in drier climates as well as in areas with longer growing seasons and more functionally diverse tree species. The importance of water availability in driving context dependency suggests that as water limitation increases under climate change, biodiversity may become even more important to support high levels of functioning in European forests.

Acetate-mediated novel survival strategy against drought in plants.

Abstract: Water deficit caused by global climate changes seriously endangers the survival of organisms and crop productivity, and increases environmental deterioration1,2. Plants' resistance to drought involves global reprogramming of transcription, cellular metabolism, hormone signalling and chromatin modification3-8. However, how these regulatory responses are coordinated via the various pathways, and the underlying mechanisms, are largely unknown. Herein, we report an essential drought-responsive network in which plants trigger a dynamic metabolic flux conversion from glycolysis into acetate synthesis to stimulate the jasmonate (JA) signalling pathway to confer drought tolerance. In Arabidopsis, the ON/OFF switching of this whole network is directly dependent on histone deacetylase HDA6. In addition, exogenous acetic acid promotes de novo JA synthesis and enrichment of histone H4 acetylation, which influences the priming of the JA signalling pathway for plant drought tolerance. This novel acetate function is evolutionarily conserved as a survival strategy against environmental changes in plants. Furthermore, the external application of acetic acid successfully enhanced the drought tolerance in Arabidopsis, rapeseed, maize, rice and wheat plants. Our findings highlight a radically new survival strategy that exploits an epigenetic switch of metabolic flux conversion and hormone signalling by which plants adapt to drought.

Mediating Effects of Place Attachment and Satisfaction on the Relationship between Tourists’ Emotions and Intention to Recommend:

Abstract: This study develops a model based on the developmental theory of place attachment. The model considers the influence of tourists’ emotions on place attachment and the mediating effects of satisfact...

Combined Analysis of Neutrino and Antineutrino Oscillations at T2K.

Abstract: T2K reports its first results in the search for CP violation in neutrino oscillations using appearance and disappearance channels for neutrino- and antineutrino-mode beam. The data include all runs from Jan 2010 to May 2016 and comprise $7.482\times10^{20}$,protons on target in neutrino mode, which yielded in the far detector 32 e-like and 135 $\mu$-like events, and $7.471\times10^{20}$,protons on target in antineutrino mode which yielded 4 e-like and 66 $\mu$-like events. Reactor measurements of $\sin^{2}2\theta_{13}$ have been used as an additional constraint. The one-dimensional confidence interval at 90% for $\delta_{CP}$ spans the range ($-3.13$, $-0.39$) for normal mass ordering. The CP conservation hypothesis ($\delta_{CP}=0,\pi$) is excluded at 90% C.L.

A Formal Security Analysis of the Signal Messaging Protocol

Abstract: Signal is a new security protocol and accompanying app that provides end-to-end encryption for instant messaging. The core protocol has recently been adopted by WhatsApp, Facebook Messenger, and Google Allo among many others, the first two of these have at least 1 billion active users. Signal includes several uncommon security properties (such as "future secrecy" or "post-compromise security"), enabled by a novel technique called ratcheting in which session keys are updated with every message sent. Despite its importance and novelty, there has been little to no academic analysis of the Signal protocol. We conduct the first security analysis of Signal's key agreement and double ratchet as a multi-stage key exchange protocol. We extract from the implementation a formal description of the abstract protocol, and define a security model which can capture the "ratcheting" key update structure. We then prove the security of Signal's core in our model, demonstrating several standard security properties. We have found no major flaws in the design, and hope that our presentation and results can serve as a starting point for other analyses of this widely adopted protocol.

Transcend: Detecting Concept Drift in Malware Classification Models

Abstract: Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming rapidly antiquated as malware continues to evolve. In this work, we propose Transcend, a framework to identify aging classification models in vivo during deployment, much before the machine learning model’s performance starts to degrade. This is a significant departure from conventional approaches that retrain aging models retrospectively when poor performance is observed. Our approach uses a statistical comparison of samples seen during deployment with those used to train the model, thereby building metrics for prediction quality. We show how Transcend can be used to identify concept drift based on two separate case studies on Android andWindows malware, raising a red flag before the model starts making consistently poor decisions due to out-of-date training.

DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware

Abstract: With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely--on their own--be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware. DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly. For malware detection, we achieve up to 99.82% accuracy with zero false positives; for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.

The Geomorphology of The Anthropocene: Emergence, Status and Implications

Abstract: The Anthropocene is proposed as a new interval of geological time in which human influence on Earth and its geological record dominates over natural processes. A major challenge in demarcating the Anthropocene is that the balance between human-influenced and natural processes varies over spatial and temporal scales owing to the inherent variability of both human activities (as associated with culture and modes of development) and natural drivers (e.g. tectonic activity and sea level variation). Against this backdrop, we consider how geomorphology might contribute towards the Anthropocene debate focussing on human impact on aeolian, fluvial, cryospheric and coastal process domains, and how evidence of this impact is preserved in landforms and sedimentary records. We also consider the evidence for an explicitly anthropogenic geomorphology that includes artificial slopes and other human-created landforms. This provides the basis for discussing the theoretical and practical contributions that geomorphology can make to defining an Anthropocene stratigraphy. It is clear that the relevance of the Anthropocene concept varies considerably amongst different branches of geomorphology, depending on the history of human actions in different process domains. For example, evidence of human dominance is more widespread in fluvial and coastal records than in aeolian and cryospheric records, so geomorphologically the Anthropocene would inevitably comprise a highly diachronous lower boundary. Even to identify this lower boundary, research would need to focus on the disambiguation of human effects on geomorphological and sedimentological signatures. This would require robust data, derived from a combination of modelling and new empirical work rather than an arbitrary ‘war of possible boundaries’ associated with convenient, but disputed, `golden spikes’. Rather than being drawn into stratigraphical debates, the primary concern of geomorphology should be with the investigation of processes and landform development, so providing the underpinning science for the study of this time of critical geological transition.

A Survey on Reinforcement Learning Models and Algorithms for Traffic Signal Control

Abstract: Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

Abstract: The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

A Comprehensive Symbolic Analysis of TLS 1.3

Abstract: The TLS protocol is intended to enable secure end-to-end communication over insecure networks, including the Internet. Unfortunately, this goal has been thwarted a number of times throughout the protocol's tumultuous lifetime, resulting in the need for a new version of the protocol, namely TLS 1.3. Over the past three years, in an unprecedented joint design effort with the academic community, the TLS Working Group has been working tirelessly to enhance the security of TLS. We further this effort by constructing the most comprehensive, faithful, and modular symbolic model of the TLS~1.3 draft 21 release candidate, and use the TAMARIN prover to verify the claimed TLS~1.3 security requirements, as laid out in draft 21 of the specification. In particular, our model covers all handshake modes of TLS 1.3. Our analysis reveals an unexpected behaviour, which we expect will inhibit strong authentication guarantees in some implementations of the protocol. In contrast to previous models, we provide a novel way of making the relation between the TLS specification and our model explicit: we provide a fully annotated version of the specification that clarifies what protocol elements we modelled, and precisely how we modelled these elements. We anticipate this model artifact to be of great benefit to the academic community and the TLS Working Group alike.

Long-term microdystrophin gene therapy is effective in a canine model of Duchenne muscular dystrophy.

Abstract: Duchenne muscular dystrophy (DMD) is an incurable X-linked muscle-wasting disease caused by mutations in the dystrophin gene. Gene therapy using highly functional microdystrophin genes and recombinant adeno-associated virus (rAAV) vectors is an attractive strategy to treat DMD. Here we show that locoregional and systemic delivery of a rAAV2/8 vector expressing a canine microdystrophin (cMD1) is effective in restoring dystrophin expression and stabilizing clinical symptoms in studies performed on a total of 12 treated golden retriever muscular dystrophy (GRMD) dogs. Locoregional delivery induces high levels of microdystrophin expression in limb musculature and significant amelioration of histological and functional parameters. Systemic intravenous administration without immunosuppression results in significant and sustained levels of microdystrophin in skeletal muscles and reduces dystrophic symptoms for over 2 years. No toxicity or adverse immune consequences of vector administration are observed. These studies indicate safety and efficacy of systemic rAAV-cMD1 delivery in a large animal model of DMD, and pave the way towards clinical trials of rAAV-microdystrophin gene therapy in DMD patients.

DELAY OF GERMINATION1 requires PP2C phosphatases of the ABA signalling pathway to control seed dormancy

Abstract: The time of seed germination is a major decision point in the life of plants determining future growth and development. This timing is controlled by seed dormancy, which prevents germination under favourable conditions. The plant hormone abscisic acid (ABA) and the protein DELAY OF GERMINATION 1 (DOG1) are essential regulators of dormancy. The function of ABA in dormancy is rather well understood, but the role of DOG1 is still unknown. Here, we describe four phosphatases that interact with DOG1 in seeds. Two of them belong to clade A of type 2C protein phosphatases: ABA-HYPERSENSITIVE GERMINATION 1 (AHG1) and AHG3. These phosphatases have redundant but essential roles in the release of seed dormancy epistatic to DOG1. We propose that the ABA and DOG1 dormancy pathways converge at clade A of type 2C protein phosphatases. The DOG1 protein is a major regulator of seed dormancy in Arabidopsis. Here, Nee et al. provide evidence that DOG1 can interact with the type 2C protein phosphatases AHG1 and AHG3 and that this represents the convergence point of the DOG1-regulated dormancy pathway and signalling by the plant hormone abscisic acid.

Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting

Abstract: The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by: 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community; 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts; and 3) providing insights on piggybacking processes. Among several findings providing insights analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggybacking operations not only concern app code, but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code.