199 research outputs found

    Ensuring data confidentiality via plausibly deniable encryption and secure deletion – a survey

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    Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger dataowners’ privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA andSarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved inthe personal computing devices (i.e.,confidentiality duringtheirlifetime); and also, they should be rendered irrecoverableafter they are removed from the devices (i.e.,confidentiality after their lifetime). Encryption and secure deletion are usedto ensure data confidentiality during and after their lifetime, respectively.This work aims to perform a thorough literature review on the techniques being used to protect confidentiality of thedata in personal computing devices, including both encryption and secure deletion. Especially for encryption, wemainly focus on the novel plausibly deniable encryption (PDE), which can ensure data confidentiality against both acoercive (i.e., the attacker can coerce the data owner for the decryption key) and a non-coercive attacker

    A new steering approach for VSCMGs with high precision

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    AbstractA new variable speed control moment gyro (VSCMG) steering law is proposed in order to achieve higher torque precision. The dynamics of VSCMGs is established, and two work modes are then designed according to command torque: control momentum gyro (CMG)/reaction wheel (RW) hybrid mode for the large torque case and RW single mode for the small. When working in the CMG/RW hybrid mode, the steering law deals with the gimbal dead-zone nonlinearity through compensation by RW sub-mode. This is in contrast to the conventional CMG singularity avoidance and wheel speed equalization, as well as the proof of definitely hyperbolic singular property of the CMG sub-mode. When working in the RW single mode, the motion of gimbals will be locked. Both the transition from CMG/RW hybrid mode to RW single mode and the reverse are studied. During the transition, wheel speed equalization and singularity avoidance of both the CMG and RW sub-modes are considered. A steering law for the RWs with locked gimbals is presented. It is shown by simulations that the VSCMGs with this new steering law could reach a better torque precision than the normal CMGs in the case of both large and small torques

    A Novel Clustering Tree-based Video lookup Strategy for Supporting VCR-like Operations in MANETs

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    Mobile Peer-to-Peer (MP2P) network is a promising avenue for large-scale deployment of Video-on-Demand (VoD) applications over mobile ad-hoc networks (MANETs). In P2P VoD systems, fast search for resources is key determinants for improving the Quality of Service (QoS) due to the low delay of seeking resources caused by streaming interactivity. In this paper, we propose a novel Clustering Tree-based Video Lookup strategy for supporting VCR-like operations in MANETs (CTVL) CTVL selects the chunks with the high popularity as "overlay router" chunks to build the "virtual connection" with other chunks in terms of the popularities and external connection of video chunks. CTVL designs a new clustering strategy to group nodes in P2P networks and a maintenance mechanism of cluster structure, which achieves the high system scalability and fast resource search performance. Thorough simulation results also show how CTVL achieves higher average lookup success rate, lower maintenance cost, lower average end-to-end delay and lower packet loss ratio (PLR) in comparison with other state of the art solutions

    Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection

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    Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV is further validated via extensive experiments on three industrial datasets

    GRAINS: Proximity Sensing of Objects in Granular Materials

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    Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and failure wedge zone). GRAINS can automatically sense buried objects beneath GM in real-time manner (at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different granules without the use of vision or touch. We introduce a new spiral trajectory for the probe raking in GM, combining linear and circular motions, inspired by a common granular fluidization technique. Based on the observation of force-raising when granular jamming occurs in the failure wedge zone in front of the probe during its raking, we employ Gaussian process regression to constantly learn and predict the force patterns and detect the force anomaly resulting from granular jamming to identify the proximity sensing of buried objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided exploration strategy to successfully localize underground objects and outline their distribution using proximity sensing without contact or digging. This work offers a simple yet reliable method with potential for safe operation in building habitation infrastructure on an alien planet without human intervention.Comment: 35 pages, 5 figures,2 tables. Videos available at https://sites.google.com/view/grains2/hom

    Ultra-high-resolution detection of Pb2+ ions using a black phosphorus functionalized microfiber coil resonator

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    A black phosphorus (BP) functionalized optical fiber sensor based on a microfiber coil resonator (MCR) for Pb2+ ion detection in an aquatic environment is presented and experimentally demonstrated. The MCR-BP sensor is manufactured by winding a tapered microfiber on a hollow rod composed of a low-refractive-index polycarbonate (PC) resin with the BP deposited on the internal wall of the rod. Based on the propagation properties of the MCR, the chemical interaction between the Pb2+ ions and the BP alters the refractive index of the ambient environment and thus results in a detectable shift in the transmission spectrum. The resonance wavelength moves towards longer wavelengths with an increasing concentration of Pb2+ ions, and the sensor has an ultra-high detection resolution of 0.0285 ppb (parts per billion). The temperature dependence is 106.95 pm/°C due to the strong thermo-optic and thermal-expansion effect of the low-refractive-index PC resin. In addition, the sensor shows good stability over a period of 15 days. The local pH also influences the sensor, with the resonance wavelength shift increasing as pH approaches a value of 7 but then decreasing as the pH value increases further due to the effect of the BP layer by H+ and OH− ions. The sensor shows the potential for high-resolution detection of Pb2+ ions in a liquid environment with the particular advantages of having a simple structure, ease of fabrication, low cost, low loss, and simple interrogation

    Deactivation Effects of Tb3+ on Ho3+ Emission in Fluoroindate Glasses for 3.9 μm Laser Applications

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    A series of Ho3+/Tb3+ co-doped fluoroindate glasses with good thermal stability have been synthesized to study the deactivation effects of Tb3+ on the Ho3+: 3.9 μm emission. Efficient 3.9 μm emission enhancement is obtained under excitation by an 888 nm laser diode (LD). The Judd-Ofelt (J-O) intensity parameters and radiative properties are calculated to evaluate the spectroscopic properties. Possible energy transfer processes resulting in emission reinforcement are discussed. A higher spontaneous transition probability and larger peak emission cross section are achieved with the inclusion of Tb3+. This analysis supports the conclusion that Ho3+/Tb3+ co-doped fluoroindate glass is a potentially useful laser material for highly efficient 3.9 μm fiber lasers

    Testing and Micromechanical Modelling of Rockfill Materials Considering the Effect of Stress Path

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    We have extended the micromechanics-based analytical (M-A) model to make it capable of simulating Nuozhadu rockfill material (NRFM) under different stress paths. Two types of drained triaxial tests on NRFM were conducted, namely, the stress paths of constant stress ratio (CSR) and the complex stress paths with transitional features. The model was improved by considering the interparticle parameter variation with the unloading-reloading cycles and the effect of the stress transition path. The evolution of local dilatancy at interparticle planes due to an externally applied load is also discussed. Compared with Duncan-Chang’s E-u and E-B models, the improved model could not only better describe the deformation properties of NRFM under the stress path loading, but also present the volumetric strain changing from dilatancy to contractancy with increasing transitional confining pressures. All simulations have demonstrated that the proposed M-A model is capable of modelling the mechanical behaviour of NRFM in the dam

    Efficiency of electrochemical chloride removal from concrete at different environmental temperatures

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    Electrochemical chloride removal (ECR) is an effective and curative method to treat existed reinforced concrete structures about to suffer or already suffering from chloride attack, however, its application is still limited due to its side effect and efficiency, including the velocity and maximum capacity of chloride removal. This paper presents a temperature related numerical transport model to study the effect of temperature on efficiency of electrochemical chloride removal from concrete. Based on Fick’s law and Nernst-Planck equation with Gauss’ Law, temperature effect, chloride binding, multi-species coupling, electrochemical reactions were taken into account in this model. Temperature effect was considered on diffusion coefficient, chloride binding, ions migration capacity as well as electrolyte concentration. The model was validated by the comparison between the calculated results and experimental data. The results indicate that temperature dose have a considerable influence on electrochemical chloride removal and controlling temperature during treatment is a practical method to improve the electrochemical chloride removal when applied current density is not amplified
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