On detecting co-resident cloud instances using network flow watermarking techniques

Virtualization is the cornerstone of the developing third-party compute industry, allowing cloud providers to instantiate multiple virtual machines (VMs) on a single set of physical resources. Customers utilize cloud resources alongside unknown and untrusted parties, creating the co-resident threat—unless perfect isolation is provided by the virtual hypervisor, there exists the possibility for unauthorized access to sensitive customer information through the exploitation of covert side channels. This paper presents co-resident watermarking, a traffic analysis attack that allows a malicious co-resident VM to inject a watermark signature into the network flow of a target instance. This watermark can be used to exfiltrate and broadcast co-residency data from the physical machine, compromising isolation without reliance on internal side channels. As a result, our approach is difficult to defend against without costly underutilization of the physical machine. We evaluate co-resident watermarkingunder a large variety of conditions, system loads and hardware configurations, from a local laboratory environment to production cloud environments (Futuregrid and the University of Oregon’s ACISS). We demonstrate the ability to initiate a covert channel of 4 bits per second, and we can confirm co-residency with a target VM instance in $<$ 10 s. We also show that passive load measurement of the target and subsequent behavior profiling is possible with this attack. We go on to consider the detectability of co-resident watermarking, extending our scheme to create a subtler watermarking attack by imitating legitimate cloud customer behavior. Our investigation demonstrates the need for the careful design of hardware to be used in the cloud.     

What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.     

Numerical simulation of non-linear chatter vibration in turning

In this paper, basic relationships and algorithms for numerical simulation of non-linear, self-excited vibrations in single degree-of-freedom cutting systems are presented. Non-linearities due to the tool leaving the cut, as well as interference between the cutting tool clearance face and cutting surface waviness, were taken into consideration. Examples of vibration simulation results are shown.     

An improved factor based approach to precedential constraint

In this article I argue for rule-based, non-monotonic theories of common law judicial reasoning and improve upon one such theory offered by Horty and Bench-Capon. The improvements reveal some of the interconnections between formal theories of judicial reasoning and traditional issues within jurisprudence regarding the notions of the ratio decidendi and obiter dicta. Though I do not purport to resolve the long-standing jurisprudential issues here, it is beneficial for theorists both of legal philosophy and formalizing legal reasoning to see where the two projects interact.     

Think,feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.     

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.     

Constructive quantum scaling of unitary matrices

In this work, we present a method of decomposition of arbitrary unitary matrix \(U\in \mathbf {U}(2^k)\) into a product of single-qubit negator and controlled-\(\sqrt{\text{ NOT }}\) gates. Since the product results with negator matrix, which can be treated as complex analogue of bistochastic matrix, our method can be seen as complex analogue of Sinkhorn–Knopp algorithm, where diagonal matrices are replaced by adding and removing an one-qubit ancilla. The decomposition can be found constructively, and resulting circuit consists of \(O(4^k)\) entangling gates, which is proved to be optimal. An example of such transformation is presented.     

In Face on Facebook: Brooklyn's Drag Community and Sociotechnical Practices of Online Communication

Recently, Brooklyn has seen an explosion of drag culture, with dozens of performers taking the stage in any given week. Social media plays a vital role for members of this community, simultaneously allowing self‐promotion and community solidarity. Drawing on focus group interviews, we analyze the communication practices of Brooklyn's drag performers, examining both the advantages and drawbacks of social media platforms. Using conceptual frameworks of faceted identity and relational labor, our discussion focuses on affordances and constraints of multifaceted identity in online contexts and theories of seamful design. We contend that by analyzing online communication practices of drag performers, it becomes possible to identify gaps between embedded ideologies of mainstream social media technologies and the localized values of outsider communities.