Strong cryptography from weak secrets

Distributed-password public-key cryptography (DPwPKC) allows the members of a group of people, each one holding a small secret password only, to help a leader to perform the private operation, associated to a public-key cryptosystem. Abdalla et al. recently defined this tool [1], with a practical construction. Unfortunately, the latter applied to the ElGamal decryption only, and relied on the DDH assumption, excluding any recent pairing-based cryptosystems. In this paper, we extend their techniques to support, and exploit, pairing-based properties: we take advantage of pairing-friendly groups to obtain efficient (simulation-sound) zero-knowledge proofs, whose security relies on the Decisional Linear assumption. As a consequence, we provide efficient protocols, secure in the standard model, for ElGamal decryption as in [1], but also for Linear decryption, as well as extraction of several identity-based cryptosystems [6,4]. Furthermore, we strenghten their security model by suppressing the useless testPwd queries in the functionality.

The University of Glasgow at Imageclefphoto 2009

In this paper we describe the approaches adopted to generate the five runs submitted to ImageClefPhoto 2009 by the University of Glasgow. The aim of our methods is to exploit document diversity in the rankings. All our runs used text statistics extracted from the captions associated to each image in the collection, except one run which combines the textual statistics with visual features extracted from the provided images. The results suggest that our methods based on text captions significantly improve the performance of the respective baselines, while the approach that combines visual features with text statistics shows lower levels of improvements.

Cryptanalysis of LASH

We show that the LASH-x hash function is vulnerable to attacks that trade time for memory, including collision attacks as fast as 2(4x/11) and preimage attacks as fast as 2(4x/7). Moreover, we briefly mention heuristic lattice based collision attacks that use small memory but require very long messages that are expected to find collisions much faster than 2 x/2. All of these attacks exploit the designers’ choice of an all zero IV. We then consider whether LASH can be patched simply by changing the IV. In this case, we show that LASH is vulnerable to a 2(7x/8) preimage attack. We also show that LASH is trivially not a PRF when any subset of input bytes is used as a secret key. None of our attacks depend upon the particular contents of the LASH matrix – we only assume that the distribution of elements is more or less uniform.

Structural abstraction of process specifications

Software engineers constantly deal with problems of designing, analyzing, and improving process specifications, e.g., source code, service compositions, or process models. Process specifications are abstractions of behavior observed or intended to be implemented in reality which result from creative engineering practice. Usually, process specifications are formalized as directed graphs in which edges capture temporal relations between decisions, synchronization points, and work activities. Every process specification is a compromise between two points: On the one hand engineers strive to operate with less modeling constructs which conceal irrelevant details, while on the other hand the details are required to achieve the desired level of customization for envisioned process scenarios. In our research, we approach the problem of varying abstraction levels of process specifications. Formally, developed abstraction mechanisms exploit the structure of a process specification and allow the generalization of low-level details into concepts of a higher abstraction level. The reverse procedure can be addressed as process specialization.

Mechanism‐based selection of a potent kallikrein‐related peptidase 7 inhibitor from a versatile library based on the sunflower trypsin inhibitor SFTI‐1

Potent and specific enzyme inhibition is a key goal in the development of therapeutic inhibitors targeting proteolytic activity. The backbone-cyclized peptide, Sunflower Trypsin Inhibitor (SFTI-1) affords a scaffold that can be engineered to achieve both these aims. SFTI-1's mechanism of inhibition is unusual in that it shows fast-on/slow-off kinetics driven by cleavage and religation of a scissile bond. This phenomenon was used to select a nanomolar inhibitor of kallikrein-related peptidase 7 (KLK7) from a versatile library of SFTI variants with diversity tailored to exploit distinctive surfaces present in the active site of serine proteases. Inhibitor selection was achieved through the use of size exclusion chromatography to separate protease/inhibitor complexes from unbound inhibitors followed by inhibitor identification according to molecular mass ascertained by mass spectrometry. This approach identified a single dominant inhibitor species with molecular weight of 1562.4 Da, which is consistent with the SFTI variant SFTI-WCTF. Once synthesized individually this inhibitor showed an IC50 of 173.9 ± 7.6 nM against chromogenic substrates and could block protein proteolysis. Molecular modeling analysis suggested that selection of SFTI-WCTF was driven by specific aromatic interactions and stabilized by an enhanced internal hydrogen bonding network. This approach provides a robust and rapid route to inhibitor selection and design.

Entrepreneurship in the public sector : new possibilities?

Previous research has described potential roles for entrepreneurs in public sector organisations as either closely related to corporate entrepreneurship, or as normative prescriptions regarding the importance of entrepreneurship in the public sector (Ireland, Covin & Kuratko, 2009: Morris & Jones, 1999). While some might argue that entrepreneurship in the public sector context is an oxymoron, recent studies have demonstrated that entrepreneurship in the public sector is alive and well (Currie, Humphreys, Ucbasaran & McManus 2008; Kim, 2010). Entrepreneurship in the public sector can take many forms and generate a range of benefits but to date less attention has been given to the potential to generate new public value (Moore, 1995). The purpose of this paper is to increase our knowledge and understanding of the types of strategies and activities the public sector is using to capture initiative, create new public value, and generate new economic activity for the benefit of multiple stakeholders. This paper explores entrepreneurship in one public sector context. Findings indicate that entrepreneurship and commercialisation is more likely to be encouraged in contexts where contestability in develop and exploit capabilities.

Joint 2D and 3D cues for image segmentation towards robotic applications

This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot traverses for robotic applications. A major theme of this thesis was to exploit the availability of 3D information acquired from robot sensors to improve upon 2D object classification alone. The proposed methods have been evaluated on several indoor and outdoor datasets collected from mobile robotic platforms including a quadcopter and ground vehicle covering several kilometres of urban roads.

Transcript and metabolite profiling for the evaluation of tobacco tree and poplar as feedstock for the bio-based industry

The global demand for food, feed, energy and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Beyond tasks and gateways: discovering BPMN models with subprocesses, boundary events and activity markers

Existing techniques for automated discovery of process models from event logs largely focus on extracting flat process models. In other words, they fail to exploit the notion of subprocess, as well as structured error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of BPMN models containing subprocesses, interrupting and non-interrupting boundary events, and loop and multi-instance markers. The technique analyzes dependencies between data attributes associated with events, in order to identify subprocesses and to extract their associated logs. Parent process and subprocess models are then discovered separately using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. A validation with one synthetic and two real-life logs shows that process models derived using the proposed technique are more accurate and less complex than those derived with flat process model discovery techniques.

Endothelial precursor cells home to a vascularized tissue engineering chamber by application of the angiogenic chemokine CXCl12

Tissue engineering of vascularized constructs has great utility in reconstructive surgery. While we have been successful in generating vascularized granulation-like tissue and adipose tissue in an in vivo tissue engineering chamber, production of other differentiated tissues in a stable construct remains a challenge. One approach is to utilize potent differentiation factors, which can influence the base tissue. Endothelial precursor cells (EPCs) have the ability to both carry differentiation factors and home to developing vasculature. In this study, proof-of-principle experiments demonstrate that such cells can be recruited from the circulation into an in vivo tissue engineering chamber. CXC chemokine ligand 12 (CXCL12)/stromal cell-derived factor 1 was infused into the chamber through Alzet osmotic pumps and chamber cannulation between days 0 and 7, and facilitated recruitment of systemically inoculated exogenous human EPCs injected on day 6. CXCL12 infusion resulted in an eightfold increase in EPC recruitment, 2 (p = 0.03) and 7 days postinfusion (p = 0.008). Delivery of chemotactic/proliferation and/or differentiation factors and appropriately timed introduction of effective cells may allow us to better exploit the regenerative potential of the established chamber construct. © Copyright 2009, Mary Ann Liebert, Inc. 2009.

Integrated access control for smart buildings using building information models

This thesis investigates the use of building information models for access control and security applications in critical infrastructures and complex building environments. It examines current problems in security management for physical and logical access control and proposes novel solutions that exploit the detailed information available in building information models. The project was carried out as part of the Airports of the Future Project and the research was modelled based on real-world problems identified in collaboration with our industry partners in the project.

Effect of atmospheric gas plasmas on cancer cell signaling

Cancer is one of the most life-threatening diseases with many forms still regarded as incurable. The conventional cancer treatments have unwanted side effects such as the death of normal cells. A therapy that can accurately target and effectively kill tumor cells could address the inadequacies of the available therapies. Atmospheric gas plasmas (AGP) that are able to specifically kill cancerous cells offer a promising alternative approach compared to conventional therapies. AGP have been shown to exploit tumor-specific genetic defects and a recent trial in mice has confirmed its antitumor effects. The mechanism by which the AGP act on tumor cells but not normal cells is not fully understood. A review of the current literature suggests that reactive oxygen species (ROS) generated by AGP induce death of cancer cells by impairing the function of intracellular regulatory factors. The majority of cancer cells are defective in tumor suppressors that interfere normal cell growth pathways. It appears that pro-oncogene or tumor suppressor-dependent regulation of antioxidant/or ROS signaling pathways may be involved in AGP-induced cancer cell death. The toxic effects of ROS are mitigated by normal cells by adjustment of their metabolic pathways. On the other hand, tumor cells are mostly defective in several regulatory signaling pathways which lead to the loss of metabolic balance within the cells and consequently, the regulation of cell growth. This review article evaluates the impact of AGP on the activation of cellular signaling and its importance for exploring mechanisms for safe and efficient anticancer therapies.

Carbon nanotube membranes with ultrahigh specific adsorption capacity for water desalination and purification

Development of technologies for water desalination and purification is critical to meet the global challenges of insufficient water supply and inadequate sanitation, especially for point-of-use applications. Conventional desalination methods are energy and operationally intensive, whereas adsorption-based techniques are simple and easy to use for point-of-use water purification, yet their capacity to remove salts is limited. Here we report that plasma-modified ultralong carbon nanotubes exhibit ultrahigh specific adsorption capacity for salt (exceeding 400% by weight) that is two orders of magnitude higher than that found in the current state-of-the-art activated carbon-based water treatment systems. We exploit this adsorption capacity in ultralong carbon nanotube-based membranes that can remove salt, as well as organic and metal contaminants. These ultralong carbon nanotube-based membranes may lead to next-generation rechargeable, point-of-use potable water purification appliances with superior desalination, disinfection and filtration properties. © 2013 Macmillan Publishers Limited.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

Understanding the value of social media in organisations : a taxonomic approach

While organizations strive to leverage the vast information generated daily from social media platforms and both decision makers and consultants are keen to identify and exploit this information’s value, there has been little research into social media in the business context. Social media are diverse, varying in scope and functionality, this diversity entailing a complex of attributes and characteristics, resulting in confusion for both researchers and organizations. Taxonomies are important precursors in emerging fields and are foundational for rigorous theory building. Though aspects of social media have been studied from various discipline perspectives, this work has been largely descriptive. Thus, while the need for a rigorous taxonomy of social media is strong, previous efforts to classify social media suffer limitations – e.g. lack of a systematic taxonomic method, overreliance on intuition, disregard for the users’ perspective, and inadequate consideration of purpose. Thus, this study was mainly initiated by the overarching question “How can social media in the business context be usefully classified?” In order to address this gap, the current paper proposes a systematic method for developing a taxonomy appropriate to study social media in organizations context, combining Nickerson et al,’s (2012) IS taxonomy building guidelines and a Repertory grid (RepGrid) approach.