Biblio

Flush-based cache attacks like Flush+Reload and Flush+Flush are highly precise and effective. Most of the flush-based attacks provide high accuracy in controlled and isolated environments where attacker and victim share OS pages. However, we observe that these attacks are prone to low accuracy on a noisy multi-core system with co-running applications. Two root causes for the varying accuracy of flush-based attacks are: (i) the dynamic nature of core frequencies that fluctuate depending on the system load, and (ii) the relative placement of victim and attacker threads in the processor, like same or different physical cores. These dynamic factors critically affect the execution latency of key instructions like clflush and mov, rendering the pre-attack calibration step ineffective.We propose DABANGG, a set of novel refinements to make flush-based attacks resilient to system noise by making them aware of frequency and thread placement. First, we introduce pre-attack calibration that is aware of instruction latency variation. Second, we use low-cost attack-time optimizations like fine-grained busy waiting and periodic feedback about the latency thresholds to improve the effectiveness of the attack. Finally, we provide victim-specific parameters that significantly improve the attack accuracy. We evaluate DABANGG-enabled Flush+Reload and Flush+Flush attacks against the standard attacks in side-channel and covert-channel experiments with varying levels of compute, memory, and IO-intensive system noise. In all scenarios, DABANGG+Flush+Reload and DABANGG+Flush+Flush outperform the standard attacks in stealth and accuracy.

In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the optimal solution. In this paper, we present a novel deep-learning-based approach for model parameter calibration in power systems. Our study focused on the generator model as an example. We studied several deep-learning-based approaches including 1-D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), which were trained to estimate model parameters using simulated Phasor Measurement Unit (PMU) data. Quantitative evaluations showed that our proposed methods can achieve high accuracy in estimating the model parameters, i.e., achieved a 0.0079 MSE on the testing dataset. We consider these promising results to be the basis for further exploration and development of advanced tools for model validation and calibration.

Human-automation shared control is proposed to reduce the risk of driver disengagement in Level-3 autonomous vehicles. Although previous studies have approved shared control strategy is effective to keep a driver in the loop and improve the driver's performance, over- and under-trust may affect the cooperation between the driver and the automation system. This study conducted a human-in-the-loop driving simulation experiment to assess the effects of trust on driver's behavior of shared control. An expert shared control strategy with longitudinal and lateral driving assistance was proposed and implemented in the experiment platform. Based on the experiment (N=24), trust in shared control was evaluated, followed by a correlation analysis of trust and behaviors. Moderating effects of trust on the relationship between gaze focalization and minimum of time to collision were then explored. Results showed that self-reported trust in shared control could be evaluated by three subscales respectively: safety, efficiency and ease of control, which all show stronger correlations with gaze focalization than other behaviors. Besides, with more trust in ease of control, there is a gentle decrease in the human-machine conflicts of mean brake inputs. The moderating effects show trust could enhance the decrease of minimum of time to collision as eyes-off-road time increases. These results indicate over-trust in automation will lead to unsafe behaviors, particularly monitoring behavior. This study contributes to revealing the link between trust and behavior in the context of human-automation shared control. It can be applied in improving the design of shared control and reducing risky behaviors of drivers by further trust calibration.

Style transfer produces a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer.To do so requires quantitative evaluation procedures, but current evaluation is qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. Our procedure relies on two statistics: the Effectiveness (E) statistic measures the extent that a given style has been transferred to the target, and the Coherence (C) statistic measures the extent to which the original image's content is preserved. Our statistics are calibrated to human preference: targets with larger values of E and C will reliably be preferred by human subjects in comparisons of style and content, respectively.We use these statistics to investigate relative performance of a number of Neural Style Transfer (NST) methods, revealing a number of intriguing properties. Admissible methods lie on a Pareto frontier (i.e. improving E reduces C, or vice versa). Three methods are admissible: Universal style transfer produces very good C but weak E; modifying the optimization used for Gatys' loss produces a method with strong E and strong C; and a modified cross-layer method has slightly better E at strong cost in C. While the histogram loss improves the E statistics of Gatys' method, it does not make the method admissible. Surprisingly, style weights have relatively little effect in improving EC scores, and most variability in transfer is explained by the style itself (meaning experimenters can be misguided by selecting styles). Our GitHub Link is available1.

Trust is a fundamental factor in how users engage in interactions with Visual Analytics (VA) systems. While the importance of building trust to this end has been pointed out in research, the aspect that trust can also be misplaced is largely ignored in VA so far. This position paper addresses this aspect by putting trust calibration in focus – i.e., the process of aligning the user’s trust with the actual trustworthiness of the VA system. To this end, we present the trust continuum in the context of VA, dissect important trust issues in both VA systems and users, as well as discuss possible approaches that can build and calibrate trust.

Ultra high frequency (UHF) partial discharge detection technology has been widely used in on-line monitoring of electrical equipment, for the influence factors of UHF signal's transfer function is complicated, the calibration of UHF method is still not realized until now. In order to study the calibration influence factors of UHF partial discharge (PD) detector, the discharge mechanism of typical PD defects is analyzed, and use a PD UHF signal simulator with multiple adjustable parameters to simulate types of PD UHF signals of electrical equipment, then performed the relative experimental research in propagation characteristics and Sensor characteristics of UHF signals. It is concluded that the calibration reliability has big differences between UHF signal energy and discharge capacity of different discharge source. The calibration curve of corona discharge and suspended discharge which can representation the severity of equipment insulation defect more accurate, and the calibration curve of internal air gap discharge and dielectric surface discharge is poorer. The distance of UHF signal energy decays to stable period become smaller with increase of frequency, and the decay of UHF signal energy is irrelevant to its frequencies when the measuring angle is changing. The frequency range of measuring UHF signal depends on effective frequency range of measurement sensor, moreover, the gain and standing-wave ratio of sensor and the energy of the received signal manifested same change trend. Therefore, in order to calibration the UHF signal, it is necessary to comprehensive consideration the specific discharge type and measuring condition. The results provide the favorable reference for a further study to build the calibration system of UHF measuring method, and to promote the effective application of UHF method in sensor characteristic fault diagnosis and insulation evaluation of electrical equipment.

Datasheets of different commercially available integrated sensors for vector measurements of magnetic fields provide typical specifications, such as measurement range, sampling rate, resolution, and noise. Other characteristics of interest, such as linearity, cross-sensitivity, remanent magnetization, and drifts over temperature, are mostly missing. This letter presents testing results of those characteristics of integrated three-dimensional (3-D) sensors working with different sensor principles and technologies in a reproducible measuring process. The sensors are exposed to temperatures from -20 °C to 80 °C and are cycled in hysteresis loops in fields up to 2.5 mT. For applying high-accuracy magnetic fields, a calibrated 3-D Helmholtz coil setup is used. Commercially available integrated 3-D magnetic field sensors are put in operation on a printed circuit board using nonmagnetic passive components. All sensors are configured for best measurement accuracy according to their data-sheets. The results show that sensors based on anisotropic magnetoresistance have high accuracy and low offsets yet also a high degree of nonlinearity. Hall-based sensors show good linearity but also high cross-sensitivity. A magnetic remanence appears for Hall-based sensors with integrated magnetic concentrators as well as for sensors using anisotropic magnetoresistance. Nearly all sensors show remaining drifts over temperature regarding offset and sensitivity up to several percentages.

The relative permittivity (also known as dielectric constant) is one of the physical properties that characterize a substance. The measurement of its magnitude can be useful in the analysis of several fluids, playing an important role in many industrial processes. This paper presents a method for measuring the relative permittivity of fluids, with the possibility of real-time monitoring. The method comprises the immersion of a capacitive sensor inside a tank or duct, in order to have the inspected substance as its dielectric. An electronic circuit is responsible for exciting this sensor, which will have its capacitance measured through a quick analysis of two analog signals outputted by the circuit. The developed capacitance meter presents a novel topology derived from the well-known Howland current source. One of its main advantages is the capacitance-selective behavior, which allows the system to overcome the effects of parasitic resistive and inductive elements on its readings. In addition to an adjustable current output that suits different impedance magnitudes, it exhibits a steady oscillating behavior, thus allowing continuous operation without any form of external control. This paper presents experimental results obtained from the proposed system and compares them to measurements made with proven and calibrated equipment. Two initial capacitance measurements performed with the system for evaluating the sensor's characteristics exhibited relative errors of approximately 0.07% and 0.53% in comparison to an accurate workbench LCR meter.

Swarm robotics, a combination of Swarm intelligence and robotics, is inspired from how the nature swarms, such as flock of birds, swarm of bees, ants, fishes etc. These group behaviours show great flexibility and robustness which enable the robots to perform various tasks like pattern formation, rescue and military operation, space expedition etc. This paper discusses an algorithm for forming patterns, which are English alphabets, by identical robots, in a finite amount of time and also analyses outcome of the algorithm. In order to implement the algorithm, 9 identical circular robots of diameter 15 cm are used, each having a Node MCU module and a rotary encoder attached to one wheel of the robot. The robots are initially placed at the centres of an imaginary 3×3 grid, on a white sheet of paper, of dimensions 250cm × 250 cm. All the robots are connected to the laptop's network via wifi and data send from the laptop is received by the Node MCU modules. This data includes the distance to be moved and the angle to be turned by each robot in order to form the letter. The rotary encoders enable the robot to move specific distances and turn specific angles, with high accuracy, by real time feedback. The algorithm is written in Python and image processing is done using OpenCV. Certain approximations are used in order to implement collision avoidance. Finally after calibration, the word given as input, is formed letter by letter, using these 9 identical robots.

Physical unclonable functions (PUFs) are devices which are easily probed but difficult to predict. Optical PUFs have been discussed within the literature, with traditional optical PUFs typically using spatial light modulators, coherent illumination, and scattering volumes; however, these systems can be large, expensive, and difficult to maintain alignment in practical conditions. We propose and demonstrate a new kind of optical PUF based on computational imaging and compressive sensing to address these challenges with traditional optical PUFs. This work describes the design, simulation, and prototyping of this computational optical PUF (COPUF) that utilizes incoherent polychromatic illumination passing through an additively manufactured refracting optical polymer element. We demonstrate the ability to pass information through a COPUF using a variety of sampling methods, including the use of compressive sensing. The sensitivity of the COPUF system is also explored. We explore non-traditional PUF configurations enabled by the COPUF architecture. The double COPUF system, which employees two serially connected COPUFs, is proposed and analyzed as a means to authenticate and communicate between two entities that have previously agreed to communicate. This configuration enables estimation of a message inversion key without the calculation of individual COPUF inversion keys at any point in the PUF life cycle. Our results show that it is possible to construct inexpensive optical PUFs using computational imaging. This could lead to new uses of PUFs in places where electrical PUFs cannot be utilized effectively, as low cost tags and seals, and potentially as authenticating and communicating devices.

Due to its low cost and availability, magnetic sensors nowadays are often incorporated into security systems to detect or localize threats. This paper, with the help of a correlated pre-published work, describes preliminary steps to ensure reliable results that could help in reducing inaccuracies/ errors in case of considering a security system that detects Magnetic IEDs employing AMR-based magnetic field sensors.

Given the complexities involved in the sensing, navigational and positioning environment on board automated vehicles we conduct an exploratory survey and identify factors capable of influencing the users' trust in such system. After the analysis of the survey data, the Situational Awareness of the Vehicle (SAV) emerges as an important factor capable of influencing the trust of the users. We follow up on that by conducting semi-structured interviews with 12 experts in the CAV field, focusing on the importance of the SAV, on the factors that are most important when talking about it as well as the need to keep the users informed regarding its status. We conclude that in the context of Connected and Automated Vehicles (CAVs), the importance of the SAV can now be expanded beyond its technical necessity of making vehicles function to a human factors area: calibrating users' trust.

This paper presents a model calibration algorithm for the modulated wideband converter (MWC) with non-ideal analog lowpass filter (LPF). The presented technique uses a test signal to estimate the finite impulse response (FIR) of the practical non-ideal LPF, and then a digital compensation filter is designed to calibrate the approximated FIR filter in the digital domain. At the cost of a moderate oversampling rate, the calibrated filter performs as an ideal LPF. The calibrated model uses the MWC system with non-ideal LPF to capture the samples of underlying signal, and then the samples are filtered by the digital compensation filter. Experimental results indicate that, without making any changes to the architecture of MWC, the proposed algorithm can obtain the samples as that of standard MWC with ideal LPF, and the signal can be reconstructed with overwhelming probability.