1,392 research outputs found
Sharp error terms for return time statistics under mixing conditions
We describe the statistics of repetition times of a string of symbols in a
stochastic process. Denote by T(A) the time elapsed until the process spells
the finite string A and by S(A) the number of consecutive repetitions of A. We
prove that, if the length of the string grows unbondedly, (1) the distribution
of T(A), when the process starts with A, is well aproximated by a certain
mixture of the point measure at the origin and an exponential law, and (2) S(A)
is approximately geometrically distributed. We provide sharp error terms for
each of these approximations. The errors we obtain are point-wise and allow to
get also approximations for all the moments of T(A) and S(A). To obtain (1) we
assume that the process is phi-mixing while to obtain (2) we assume the
convergence of certain contidional probabilities
Formal Verification of Security Protocol Implementations: A Survey
Automated formal verification of security protocols has been mostly focused on analyzing high-level abstract models which, however, are significantly different from real protocol implementations written in programming languages. Recently, some researchers have started investigating techniques that bring automated formal proofs closer to real implementations. This paper surveys these attempts, focusing on approaches that target the application code that implements protocol logic, rather than the libraries that implement cryptography. According to these approaches, libraries are assumed to correctly implement some models. The aim is to derive formal proofs that, under this assumption, give assurance about the application code that implements the protocol logic. The two main approaches of model extraction and code generation are presented, along with the main techniques adopted for each approac
Prochlo: Strong Privacy for Analytics in the Crowd
The large-scale monitoring of computer users' software activities has become
commonplace, e.g., for application telemetry, error reporting, or demographic
profiling. This paper describes a principled systems architecture---Encode,
Shuffle, Analyze (ESA)---for performing such monitoring with high utility while
also protecting user privacy. The ESA design, and its Prochlo implementation,
are informed by our practical experiences with an existing, large deployment of
privacy-preserving software monitoring.
(cont.; see the paper
A Cost-based Optimizer for Gradient Descent Optimization
As the use of machine learning (ML) permeates into diverse application
domains, there is an urgent need to support a declarative framework for ML.
Ideally, a user will specify an ML task in a high-level and easy-to-use
language and the framework will invoke the appropriate algorithms and system
configurations to execute it. An important observation towards designing such a
framework is that many ML tasks can be expressed as mathematical optimization
problems, which take a specific form. Furthermore, these optimization problems
can be efficiently solved using variations of the gradient descent (GD)
algorithm. Thus, to decouple a user specification of an ML task from its
execution, a key component is a GD optimizer. We propose a cost-based GD
optimizer that selects the best GD plan for a given ML task. To build our
optimizer, we introduce a set of abstract operators for expressing GD
algorithms and propose a novel approach to estimate the number of iterations a
GD algorithm requires to converge. Extensive experiments on real and synthetic
datasets show that our optimizer not only chooses the best GD plan but also
allows for optimizations that achieve orders of magnitude performance speed-up.Comment: Accepted at SIGMOD 201
A Machine-Checked Formalization of the Generic Model and the Random Oracle Model
Most approaches to the formal analyses of cryptographic protocols make the perfect cryptography assumption, i.e. the hypothese that there is no way to obtain knowledge about the plaintext pertaining to a ciphertext without knowing the key. Ideally, one would prefer to rely on a weaker hypothesis on the computational cost of gaining information about the plaintext pertaining to a ciphertext without knowing the key. Such a view is permitted by the Generic Model and the Random Oracle Model which provide non-standard computational models in which one may reason about the computational cost of breaking a cryptographic scheme. Using the proof assistant Coq, we provide a machine-checked account of the Generic Model and the Random Oracle Mode
High throughput indexing for large-scale semantic web data
Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Currently, several such systems have been proposed, however, their indexing methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose a high throughout index to enable rapid analysis of large datasets. We adopt a hybrid structure to combine the loading speed of similar-size based methods with the execution speed of graph-based approaches, using dynamic data repartitioning over query workloads. We introduce the design and detailed implementation of our method. Experimental results show that the proposed index can indeed vastly improve loading speeds while remaining competitive in terms of performance. Therefore, the method could be considered as a good choice for RDF analysis in large-scale distributed scenarios
Single Shot Temporal Action Detection
Temporal action detection is a very important yet challenging problem, since
videos in real applications are usually long, untrimmed and contain multiple
action instances. This problem requires not only recognizing action categories
but also detecting start time and end time of each action instance. Many
state-of-the-art methods adopt the "detection by classification" framework:
first do proposal, and then classify proposals. The main drawback of this
framework is that the boundaries of action instance proposals have been fixed
during the classification step. To address this issue, we propose a novel
Single Shot Action Detector (SSAD) network based on 1D temporal convolutional
layers to skip the proposal generation step via directly detecting action
instances in untrimmed video. On pursuit of designing a particular SSAD network
that can work effectively for temporal action detection, we empirically search
for the best network architecture of SSAD due to lacking existing models that
can be directly adopted. Moreover, we investigate into input feature types and
fusion strategies to further improve detection accuracy. We conduct extensive
experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When
setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD
significantly outperforms other state-of-the-art systems by increasing mAP from
19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201
A Reflection on Types
The ability to perform type tests at runtime blurs the line between statically-typed and dynamically-checked languages. Recent developments in Haskell’s type system allow even programs that use reflection to themselves be statically typed, using a type-indexed runtime representation of types called \{}\textit{TypeRep}. As a result we can build dynamic types as an ordinary, statically-typed library, on top of \{}\textit{TypeRep} in an open-world context
Safe abstractions of data encodings in formal security protocol models
When using formal methods, security protocols are usually modeled at a high level of abstraction. In particular, data encoding and decoding transformations are often abstracted away. However, if no assumptions at all are made on the behavior of such transformations, they could trivially lead to security faults, for example leaking secrets or breaking freshness by collapsing nonces into constants. In order to address this issue, this paper formally states sufficient conditions, checkable on sequential code, such that if an abstract protocol model is secure under a Dolev-Yao adversary, then a refined model, which takes into account a wide class of possible implementations of the encoding/decoding operations, is implied to be secure too under the same adversary model. The paper also indicates possible exploitations of this result in the context of methods based on formal model extraction from implementation code and of methods based on automated code generation from formally verified model
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