557 research outputs found
The STRESS Method for Boundary-point Performance Analysis of End-to-end Multicast Timer-Suppression Mechanisms
Evaluation of Internet protocols usually uses random scenarios or scenarios
based on designers' intuition. Such approach may be useful for average-case
analysis but does not cover boundary-point (worst or best-case) scenarios. To
synthesize boundary-point scenarios a more systematic approach is needed.In
this paper, we present a method for automatic synthesis of worst and best case
scenarios for protocol boundary-point evaluation.
Our method uses a fault-oriented test generation (FOTG) algorithm for
searching the protocol and system state space to synthesize these scenarios.
The algorithm is based on a global finite state machine (FSM) model. We extend
the algorithm with timing semantics to handle end-to-end delays and address
performance criteria. We introduce the notion of a virtual LAN to represent
delays of the underlying multicast distribution tree. The algorithms used in
our method utilize implicit backward search using branch and bound techniques
and start from given target events. This aims to reduce the search complexity
drastically. As a case study, we use our method to evaluate variants of the
timer suppression mechanism, used in various multicast protocols, with respect
to two performance criteria: overhead of response messages and response time.
Simulation results for reliable multicast protocols show that our method
provides a scalable way for synthesizing worst-case scenarios automatically.
Results obtained using stress scenarios differ dramatically from those obtained
through average-case analyses. We hope for our method to serve as a model for
applying systematic scenario generation to other multicast protocols.Comment: 24 pages, 10 figures, IEEE/ACM Transactions on Networking (ToN) [To
appear
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
User preference profiling is an important task in modern online social
networks (OSN). With the proliferation of image-centric social platforms, such
as Pinterest, visual contents have become one of the most informative data
streams for understanding user preferences. Traditional approaches usually
treat visual content analysis as a general classification problem where one or
more labels are assigned to each image. Although such an approach simplifies
the process of image analysis, it misses the rich context and visual cues that
play an important role in people's perception of images. In this paper, we
explore the possibilities of learning a user's latent visual preferences
directly from image contents. We propose a distance metric learning method
based on Deep Convolutional Neural Networks (CNN) to directly extract
similarity information from visual contents and use the derived distance metric
to mine individual users' fine-grained visual preferences. Through our
preliminary experiments using data from 5,790 Pinterest users, we show that
even for the images within the same category, each user possesses distinct and
individually-identifiable visual preferences that are consistent over their
lifetime. Our results underscore the untapped potential of finer-grained visual
preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop
Data communications via cable television networks : technical and policy considerations
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1983.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERINGBibliography: leaves 144-151.by Deborah Lynn Estrin.M.S
Recovering Temporal Integrity with Data Driven Time Synchronization
Data Driven Time Synchronization (DDTS) provides
synchronization across sensors by using underlying characteristics of data collected by an embedded sensing sys-
tem. We apply the concept of Data Driven Time Synchronization through a seismic deployment consisting
of 100 seismic sensors to repair data that was not time
synchronized correctly. This deployment used GPS for
time synchronization but due to system faults common
to environmental sensing systems, data was collected
with large time offsets. In seismic deployments, offset
data is often never used but we show that Data Driven
Time Synchronization can recover the synchronization
and make the data usable. To implement Data Driven
Time Synchronization to repair the time offsets we use
microseisms as the underlying characteristics. Microseisms are waves that travel through the earth’s crust
and are independent of the seismic events used for the
study of the earth’s structure. We have developed a
model of microseism propagation through a linear seismic array and use the model to obtain time correction
shifts. By simulating time offsets in real data which does
not have offsets, we determined that this method is able
to repair the offset to less than 0.2 seconds. Our ongoing work will attempt to refine the model to correct the
offsets to 0.05 seconds and evaluate how errors in the
correction affect seismic results such as event location.
Data Driven Time Synchronization may be applicable
to other high data rate embedded sensing applications
such as acoustic source localization
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