41,430 research outputs found
A low-speed BIST framework for high-performance circuit testing
Testing of high performance integrated circuits is becoming increasingly a challenging task owing to high clock frequencies. Often testers are not able to test such devices due to their limited high frequency capabilities. In this article we outline a design-for-test methodology such that high performance devices can be tested on relatively low performance testers. In addition, a BIST framework is discussed based on this methodology. Various implementation aspects of this technique are also addresse
Video Tester -- A multiple-metric framework for video quality assessment over IP networks
This paper presents an extensible and reusable framework which addresses the
problem of video quality assessment over IP networks. The proposed tool
(referred to as Video-Tester) supports raw uncompressed video encoding and
decoding. It also includes different video over IP transmission methods (i.e.:
RTP over UDP unicast and multicast, as well as RTP over TCP). In addition, it
is furnished with a rich set of offline analysis capabilities. Video-Tester
analysis includes QoS and bitstream parameters estimation (i.e.: bandwidth,
packet inter-arrival time, jitter and loss rate, as well as GOP size and
I-frame loss rate). Our design facilitates the integration of virtually any
existing video quality metric thanks to the adopted Python-based modular
approach. Video-Tester currently provides PSNR, SSIM, ITU-T G.1070 video
quality metric, DIV and PSNR-based MOS estimations. In order to promote its use
and extension, Video-Tester is open and publicly available.Comment: 5 pages, 5 figures. For the Google Code project, see
http://video-tester.googlecode.com
Exploring Differential Obliviousness
In a recent paper, Chan et al. [SODA \u2719] proposed a relaxation of the notion of (full) memory obliviousness, which was introduced by Goldreich and Ostrovsky [J. ACM \u2796] and extensively researched by cryptographers. The new notion, differential obliviousness, requires that any two neighboring inputs exhibit similar memory access patterns, where the similarity requirement is that of differential privacy. Chan et al. demonstrated that differential obliviousness allows achieving improved efficiency for several algorithmic tasks, including sorting, merging of sorted lists, and range query data structures.
In this work, we continue the exploration of differential obliviousness, focusing on algorithms that do not necessarily examine all their input. This choice is motivated by the fact that the existence of logarithmic overhead ORAM protocols implies that differential obliviousness can yield at most a logarithmic improvement in efficiency for computations that need to examine all their input. In particular, we explore property testing, where we show that differential obliviousness yields an almost linear improvement in overhead in the dense graph model, and at most quadratic improvement in the bounded degree model. We also explore tasks where a non-oblivious algorithm would need to explore different portions of the input, where the latter would depend on the input itself, and where we show that such a behavior can be maintained under differential obliviousness, but not under full obliviousness. Our examples suggest that there would be benefits in further exploring which class of computational tasks are amenable to differential obliviousness
Evading Classifiers by Morphing in the Dark
Learning-based systems have been shown to be vulnerable to evasion through
adversarial data manipulation. These attacks have been studied under
assumptions that the adversary has certain knowledge of either the target model
internals, its training dataset or at least classification scores it assigns to
input samples. In this paper, we investigate a much more constrained and
realistic attack scenario wherein the target classifier is minimally exposed to
the adversary, revealing on its final classification decision (e.g., reject or
accept an input sample). Moreover, the adversary can only manipulate malicious
samples using a blackbox morpher. That is, the adversary has to evade the
target classifier by morphing malicious samples "in the dark". We present a
scoring mechanism that can assign a real-value score which reflects evasion
progress to each sample based on the limited information available. Leveraging
on such scoring mechanism, we propose an evasion method -- EvadeHC -- and
evaluate it against two PDF malware detectors, namely PDFRate and Hidost. The
experimental evaluation demonstrates that the proposed evasion attacks are
effective, attaining evasion rate on the evaluation dataset.
Interestingly, EvadeHC outperforms the known classifier evasion technique that
operates based on classification scores output by the classifiers. Although our
evaluations are conducted on PDF malware classifier, the proposed approaches
are domain-agnostic and is of wider application to other learning-based
systems
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Overcoming controllability problems with fewest channels between testers
When testing a system that has multiple physically distributed
ports/interfaces it is normal to place a tester at each port. Each
tester observes only the events at its port and it is known that
this can lead to additional controllability problems. While such
controllability problems can be overcome by the exchange of
external coordination messages between the testers, this requires
the deployment of an external network and may thus increase the
costs of testing. The problem studied in this paper is finding a
minimum number of coordination channels to overcome
controllability problems in distributed testing. Three instances
of this problem are considered. The first problem is to find a
minimum number of channels between testers in order to overcome
the controllability problems in a given test sequence to be
applied in testing. The second problem is finding a minimal set of
channels that allow us to overcome controllability problems in any
test sequence that may be selected from the specification of the
system under test. The last problem is to find a test sequence
that achieves a particular test objective and in doing so allows
fewest channels to be used
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