8,488 research outputs found
Detecting Flow Anomalies in Distributed Systems
Deep within the networks of distributed systems, one often finds anomalies
that affect their efficiency and performance. These anomalies are difficult to
detect because the distributed systems may not have sufficient sensors to
monitor the flow of traffic within the interconnected nodes of the networks.
Without early detection and making corrections, these anomalies may aggravate
over time and could possibly cause disastrous outcomes in the system in the
unforeseeable future. Using only coarse-grained information from the two end
points of network flows, we propose a network transmission model and a
localization algorithm, to detect the location of anomalies and rank them using
a proposed metric within distributed systems. We evaluate our approach on
passengers' records of an urbanized city's public transportation system and
correlate our findings with passengers' postings on social media microblogs.
Our experiments show that the metric derived using our localization algorithm
gives a better ranking of anomalies as compared to standard deviation measures
from statistical models. Our case studies also demonstrate that transportation
events reported in social media microblogs matches the locations of our detect
anomalies, suggesting that our algorithm performs well in locating the
anomalies within distributed systems
Are the dimensions of private information more multiple than expected? Information asymmetries in the market of supplementary private health insurance in England
Our study reexamines standard econometric approaches for the detection of information asymmetries on insurance markets. We claim that evidence based on a standard framework with 2 equations, which uses potential sources of information asymmetries, should stress the importance of heterogeneity in the parameters. We argue that conclusions derived from this methodology can be misleading if the estimated coefficients in such an `unused characteristics' framework are driven by different parts of the population.
We show formally that an individual's expected risk from the perspective of insurance, conditioned on certain characteristics (which are not used for calculating the risk premium), can equal the population's expectation in risk { although such characteristics are both related to risk and insurance probability, which is usually interpreted as an indicator of information asymmetries.
We provide empirical evidence on the existence of information asymmetries in the market for supplementary private health insurance in the UK. Overall, we found evidence for advantageous selection into the private risk pool; ie people with lower health risk tend to insure more. The main drivers of this phenomenon seem to be characteristics such as income and wealth. Nevertheless, we also found parameter heterogeneity to be relevant, leading to possible misinterpretation if the standard `unused characteristics' approach is applied
Testing methods and techniques - A compilation
Nondestructive test methods and techniques including computer programs and data reductio
Distribution Grid Line Outage Detection with Privacy Data
Change point detection is important for many real-world applications. While
sensor readings enable line outage identification, they bring privacy concerns
by allowing an adversary to divulge sensitive information such as household
occupancy and economic status. In this paper, to preserve privacy, we develop a
decentralized randomizing scheme to ensure no direct exposure of each user's
raw data. Brought by the randomizing scheme, the trade-off between privacy gain
and degradation of change point detection performance is quantified via
studying the differential privacy framework and the Kullback-Leibler
divergence. Furthermore, we propose a novel statistic to mitigate the impact of
randomness, making our detection procedure both privacy-preserving and have
optimal performance. The results of comprehensive experiments show that our
proposed framework can effectively find the outage with privacy guarantees.Comment: 5 page
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy
We describe a framework for designing efficient active learning algorithms
that are tolerant to random classification noise and are
differentially-private. The framework is based on active learning algorithms
that are statistical in the sense that they rely on estimates of expectations
of functions of filtered random examples. It builds on the powerful statistical
query framework of Kearns (1993).
We show that any efficient active statistical learning algorithm can be
automatically converted to an efficient active learning algorithm which is
tolerant to random classification noise as well as other forms of
"uncorrelated" noise. The complexity of the resulting algorithms has
information-theoretically optimal quadratic dependence on , where
is the noise rate.
We show that commonly studied concept classes including thresholds,
rectangles, and linear separators can be efficiently actively learned in our
framework. These results combined with our generic conversion lead to the first
computationally-efficient algorithms for actively learning some of these
concept classes in the presence of random classification noise that provide
exponential improvement in the dependence on the error over their
passive counterparts. In addition, we show that our algorithms can be
automatically converted to efficient active differentially-private algorithms.
This leads to the first differentially-private active learning algorithms with
exponential label savings over the passive case.Comment: Extended abstract appears in NIPS 201
A reasonable benchmarking frontier using DEA : an incentive scheme to improve efficiency in public hospitals
There exists research relating management concepts with productivity measurement methods that
offers useful solutions for improving management control in the public sector. Within this sphere,
we connect agency theory with efficiency analysis and describe how to define an incentives
scheme that can be applied in the public sector to monitor the efficiency and productivity of
managers. To fulfill the main objective of this research, we propose an iterative process for
determining what we define as a ‘reasonable frontier’, a concept that provides the foundation
required to establish the incentive scheme for the managers. Our ‘reasonable frontier’ has the
following properties: i) it detects the presence of outliers, ii) it proposes a procedure to establish
the influence introduced by extreme observations, and iii) it sorts out the problem of data masking.
The proposed method is applied to a sample of hospitals taken from the public network of the
Spanish health service. The results obtained confirm the applicability of the proposal made.
Summing up, we define and apply a useful method, combining aspects of agency theory and
efficiency analysis, which is of interest to those public authorities trying to design effective
incentive schemes which influence the decision making of the public managers
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