TrustShadow: Secure Execution of Unmodified Applications with ARM TrustZone

The rapid evolution of Internet-of-Things (IoT) technologies has led to an emerging need to make it smarter. A variety of applications now run simultaneously on an ARM-based processor. For example, devices on the edge of the Internet are provided with higher horsepower to be entrusted with storing, processing and analyzing data collected from IoT devices. This significantly improves efficiency and reduces the amount of data that needs to be transported to the cloud for data processing, analysis and storage. However, commodity OSes are prone to compromise. Once they are exploited, attackers can access the data on these devices. Since the data stored and processed on the devices can be sensitive, left untackled, this is particularly disconcerting. In this paper, we propose a new system, TrustShadow that shields legacy applications from untrusted OSes. TrustShadow takes advantage of ARM TrustZone technology and partitions resources into the secure and normal worlds. In the secure world, TrustShadow constructs a trusted execution environment for security-critical applications. This trusted environment is maintained by a lightweight runtime system that coordinates the communication between applications and the ordinary OS running in the normal world. The runtime system does not provide system services itself. Rather, it forwards requests for system services to the ordinary OS, and verifies the correctness of the responses. To demonstrate the efficiency of this design, we prototyped TrustShadow on a real chip board with ARM TrustZone support, and evaluated its performance using both microbenchmarks and real-world applications. We showed TrustShadow introduces only negligible overhead to real-world applications.Comment: MobiSys 201

Comment on ``Reduction of static field equation of Faddeev model to first order PDE'', arXiv:0707.2207

The authors of the article Phys. Lett. B 652 (2007) 384, (arXiv:0707.2207), propose an interesting method to solve the Faddeev model by reducing it to a set of first order PDEs. They first construct a vectorial quantity $\bm \alpha$, depending on the original field and its first derivatives, in terms of which the field equations reduce to a linear first order equation. Then they find vectors $\bm \alpha_1$ and $\bm \alpha_2$ which identically obey this linear first order equation. The last step consists in the identification of the $\bm \alpha_i$ with the original $\bm \alpha$ as a function of the original field. Unfortunately, the derivation of this last step in the paper cited above contains an error which invalidates most of its results

Multidimensional Scaling of Varietal Data in Sedimentary Provenance Analysis

Varietal studies of sedimentary provenance use the properties of individual minerals or mineral groups. These are recorded as lists of numerical tables that can be difficult to interpret. Multidimensional Scaling (MDS) is a popular multivariate ordination technique for analyzing other types of provenance data based on, for example, detrital geochronology or petrography. Applying MDS to varietal data would allow them to be treated on an equal footing with those other provenance proxies. MDS requires a method to quantify the dissimilarity between two samples. This paper introduces three ways to do so. The first method (“treatment-by-row”) turns lists of (compositional) data tables into lists of vectors, using principal component analysis. These lists of vectors can then be treated as “distributional” data and subjected to MDS analysis using dissimilarity measures such as the Kolmogorov-Smirnov statistic. The second method (“treatment-by-column”) turns lists of compositional data tables into multiple lists of vectors, each representing a single component of the varietal data. These multiple distributional data sets are subsequently subjected to Procrustes analysis or 3-way MDS. The third method uses the Wasserstein-2 distance to jointly compare the rows and columns of varietal data. This arguably makes the best use of the data but acts more like a “black box” than the other two methods. Applying the three methods to a detrital titanite data set from Colombia yields similar results. After converting varietal data to dissimilarity matrices, they can be combined with other types of provenance data, again using Procrustes analysis or 3-way MDS

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed

Two Hands on the Wheel: Steering Robotics Innovation in Useful Directions

Practices of innovation are not autonomous. Research in STS and innovation studies has shown both the speed of knowledge production, and crucially its direction, may be susceptible to change. Indeed, interventions to the rate and direction of innovation are crucial if we are to address address the transformations needed in the economy and wider society that might for example avoid the extremes of climate change and meet the sustainable development goals. Yet interventions in these regards remain inexact. Innovation policy is one way in which government seeks to drive the production of policy towards or away from specific ends. Recent initiatives such as efforts to include "co-creation" have sought to open up innovation practices to a wider range of actors, broadening participation. But what arrangements of objects, sites, publics, and concepts do these instruments create? And how might these arrangements contribute to the laudable if lofty goals of steering innovation in useful directions? The paper follows two innovation instruments designed to influence innovation in the domain of robotics; the establishment of a "certified testbed" and a "co-creation facility". The paper asks how do co-creation instruments in the field of robotics steer innovation towards social progress or otherwise? Using a situated analysis method, this paper traces the two instruments in and around a single robotics innovation facility in the United Kingdom

Human pathogen shown to cause disease in the threatened elkhorn coral Acropora palmata

Coral reefs are in severe decline. Infections by the human pathogen Serratia marcescens have contributed to precipitous losses in the common Caribbean elkhorn coral, Acropora palmata, culminating in its listing under the United States Endangered Species Act. During a 2003 outbreak of this coral disease, called acroporid serratiosis (APS), a unique strain of the pathogen, Serratia marcescens strain PDR60, was identified from diseased A. palmata, human wastewater, the non-host coral Siderastrea siderea and the corallivorous snail Coralliophila abbreviata. In order to examine humans as a source and other marine invertebrates as vectors and/or reservoirs of the APS pathogen, challenge experiments were conducted with A. palmata maintained in closed aquaria to determine infectivity of strain PDR60 from reef and wastewater sources. Strain PDR60 from wastewater and diseased A. palmata caused disease signs in elkhorn coral in as little as four and five days, respectively, demonstrating that wastewater is a definitive source of APS and identifying human strain PDR60 as a coral pathogen through fulfillment of Koch\u27s postulates. A. palmata inoculated with strain PDR60 from C. abbreviata showed limited virulence, with one of three inoculated fragments developing APS signs within 13 days. Strain PDR60 from non-host coral S. siderea showed a delayed pathogenic effect, with disease signs developing within an average of 20 days. These results suggest that C. abbreviata and non-host corals may function as reservoirs or vectors of the APS pathogen. Our results provide the first example of a marine “reverse zoonosis” involving the transmission of a human pathogen (S. marcescens) to a marine invertebrate (A. palmata). These findings underscore the interaction between public health practices and environmental health indices such as coral reef survival