341 research outputs found
Approximating Cumulative Pebbling Cost Is Unique Games Hard
The cumulative pebbling complexity of a directed acyclic graph is defined
as , where the minimum is taken over all
legal (parallel) black pebblings of and denotes the number of
pebbles on the graph during round . Intuitively, captures
the amortized Space-Time complexity of pebbling copies of in parallel.
The cumulative pebbling complexity of a graph is of particular interest in
the field of cryptography as is tightly related to the
amortized Area-Time complexity of the Data-Independent Memory-Hard Function
(iMHF) [AS15] defined using a constant indegree directed acyclic
graph (DAG) and a random oracle . A secure iMHF should have
amortized Space-Time complexity as high as possible, e.g., to deter brute-force
password attacker who wants to find such that . Thus, to
analyze the (in)security of a candidate iMHF , it is crucial to
estimate the value but currently, upper and lower bounds for
leading iMHF candidates differ by several orders of magnitude. Blocki and Zhou
recently showed that it is -Hard to compute , but
their techniques do not even rule out an efficient
-approximation algorithm for any constant . We
show that for any constant , it is Unique Games hard to approximate
to within a factor of .
(See the paper for the full abstract.)Comment: 28 pages, updated figures and corrected typo
Domain Alignment and Temporal Aggregation for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims at detecting and segmenting the
most salient object in videos. In recent times, two-stream approaches that
collaboratively leverage appearance cues and motion cues have attracted
extensive attention thanks to their powerful performance. However, there are
two limitations faced by those methods: 1) the domain gap between appearance
and motion information is not well considered; and 2) long-term temporal
coherence within a video sequence is not exploited. To overcome these
limitations, we propose a domain alignment module (DAM) and a temporal
aggregation module (TAM). DAM resolves the domain gap between two modalities by
forcing the values to be in the same range using a cross-correlation mechanism.
TAM captures long-term coherence by extracting and leveraging global cues of a
video. On public benchmark datasets, our proposed approach demonstrates its
effectiveness, outperforming all existing methods by a substantial margin
On the Security of Proofs of Sequential Work in a Post-Quantum World
A Proof of Sequential Work (PoSW) allows a prover to convince a
resource-bounded verifier that the prover invested a substantial amount of
sequential time to perform some underlying computation. PoSWs have many
applications including time-stamping, blockchain design, and universally
verifiable CPU benchmarks. Mahmoody, Moran, and Vadhan (ITCS 2013) gave the
first construction of a PoSW in the random oracle model though the construction
relied on expensive depth-robust graphs. In a recent breakthrough, Cohen and
Pietrzak (EUROCRYPT 2018) gave an efficient PoSW construction that does not
require expensive depth-robust graphs.
In the classical parallel random oracle model, it is straightforward to argue
that any successful PoSW attacker must produce a long -sequence
and that any malicious party running in sequential time will fail to
produce an -sequence of length except with negligible
probability. In this paper, we prove that any quantum attacker running in
sequential time will fail to produce an -sequence except
with negligible probability -- even if the attacker submits a large batch of
quantum queries in each round. The proof is substantially more challenging and
highlights the power of Zhandry's recent compressed oracle technique (CRYPTO
2019). We further extend this result to establish post-quantum security of a
non-interactive PoSW obtained by applying the Fiat-Shamir transform to Cohen
and Pietrzak's efficient construction (EUROCRYPT 2018).Comment: 44 pages, 4 figure
Dynamic human resource selection for business process exceptions
A key capability of today's organizations is to flexibly and effectively react to unexpected events. A critical case of an unexpected event is sudden unavailability of human resources, which was not properly addressed by existing resource allocation approaches. This paper proposes a systematic approach that analyzes event logs to select suitable substitutes if the initial human resources become unavailable. The approach uses process mining and social network analysis to derive a metric called degree of substitution, which measures how much the work experiences of the human resources overlap, from the two perspectives: task execution and transfer of work. Along with the metric, suitable substitutes are also identified. A simulation demonstrates that the approach identifies suitable substitutes more effectively and accurately than existing allocation methods such as role‐based allocation or random allocation. The proposed approach will increase the effectiveness of dynamic allocation of human resources, especially in an exceptional situation.11Yscopu
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