917 research outputs found
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Outsourcing data storage to the remote cloud can be an economical solution to
enhance data management in the smart grid ecosystem. To protect the privacy of
data, the utility company may choose to encrypt the data before uploading them
to the cloud. However, while encryption provides confidentiality to data, it
also sacrifices the data owners' ability to query a special segment in their
data. Searchable symmetric encryption is a technology that enables users to
store documents in ciphertext form while keeping the functionality to search
keywords in the documents. However, most state-of-the-art SSE algorithms are
only focusing on general document storage, which may become unsuitable for
smart grid applications. In this paper, we propose a simple, practical SSE
scheme that aims to protect the privacy of data generated in the smart grid.
Our scheme achieves high space complexity with small information disclosure
that was acceptable for practical smart grid application. We also implement a
prototype over the statistical data of advanced meter infrastructure to show
the effectiveness of our approach
Lying Aversion and Vague Communication: An Experimental Study
An agent may strategically employ a vague message to mislead an audience's
belief about the state of the world, but this may cause the agent to feel guilt
or negatively impact how the audience perceives the agent. Using a novel
experimental design that allows participants to be vague while at the same time
isolating the internal cost of lying from the social identity cost of appearing
dishonest, we explore the extent to which these two types of lying costs affect
communication. We find that participants exploit vagueness to be consistent
with the truth, while at the same time leveraging the imprecision to their own
benefit. More participants use vague messages in treatments where concern with
social identity is relevant. In addition, we find that social identity concerns
substantially affect the length and patterns of vague messages used across the
treatments
Using Adaptive Psychophysics to Identify the Neural Network Reset Time in Subsecond Interval Timing
State dependent network models of subsecond interval timing propose that duration is encoded in states of neuronal populations that need to reset prior to a novel timing operation in order to maintain optimal timing performance. Previous research has shown that the approximate boundary of this reset interval can be inferred by varying the interstimulus interval between two to-be-timed intervals. However, the estimated boundary of this reset interval is broad (250-500ms) and remains underspecified with implications for the characteristics of state dependent network dynamics subserving interval timing. Here we probed the interval specificity of this reset boundary by manipulating the interstimulus interval between standard and comparison intervals in two subsecond auditory duration discrimination tasks (100 and 200ms) and a control (pitch) discrimination task using adaptive psychophysics. We found that discrimination thresholds improved with the introduction of a 333ms interstimulus interval relative to a 250ms interstimulus interval in both duration discrimination tasks, but not in the control task. This effect corroborates previous findings of a breakpoint in the discrimination performance for subsecond stimulus interval pairs as a function of an incremental interstimulus delay but more precisely localizes the minimal interstimulus delay range. These results suggest that state dependent networks subserving subsecond timing require approximately 250-333ms for the network to reset in order to maintain optimal interval timing
How good are Global Newton methods? Part 2
Newton's method applied to certain problems with a discontinuous derivative operator is shown to be effective. A global Newton method in this setting is exhibited and its computational complexity is estimated. As an application a method is proposed to solve problems of linear inequalities (linear programming, phase 1). Using an example of the Klee-Minty type due to Blair, it was found that the simplex method (used in super-lindo) required over 2,000 iterations, while the method above required an average of 8 iterations (Newton steps) over 15 random starting values.Naval Surface Weapons Center, Dahlgren, VAhttp://archive.org/details/howgoodareglobal00goldO&MN Direct FundingApproved for public release; distribution is unlimited
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model
The widespread adoption of Image Processing has propelled Object Recognition
(OR) models into essential roles across various applications, demonstrating the
power of AI and enabling crucial services. Among the applications, traffic sign
recognition stands out as a popular research topic, given its critical
significance in the development of autonomous vehicles. Despite their
significance, real-world challenges, such as alterations to traffic signs, can
negatively impact the performance of OR models. This study investigates the
influence of altered traffic signs on the accuracy and effectiveness of object
recognition, employing a publicly available dataset to introduce alterations in
shape, color, content, visibility, angles and background. Focusing on the
YOLOv7 (You Only Look Once) model, the study demonstrates a notable decline in
detection and classification accuracy when confronted with traffic signs in
unusual conditions including the altered traffic signs. Notably, the
alterations explored in this study are benign examples and do not involve
algorithms used for generating adversarial machine learning samples. This study
highlights the significance of enhancing the robustness of object detection
models in real-life scenarios and the need for further investigation in this
area to improve their accuracy and reliability
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