1,780 research outputs found
Coding for interactive communication correcting insertions and deletions
We consider the question of interactive communication, in which two remote
parties perform a computation while their communication channel is
(adversarially) noisy. We extend here the discussion into a more general and
stronger class of noise, namely, we allow the channel to perform insertions and
deletions of symbols. These types of errors may bring the parties "out of
sync", so that there is no consensus regarding the current round of the
protocol.
In this more general noise model, we obtain the first interactive coding
scheme that has a constant rate and resists noise rates of up to
. To this end we develop a novel primitive we name edit
distance tree code. The edit distance tree code is designed to replace the
Hamming distance constraints in Schulman's tree codes (STOC 93), with a
stronger edit distance requirement. However, the straightforward generalization
of tree codes to edit distance does not seem to yield a primitive that suffices
for communication in the presence of synchronization problems. Giving the
"right" definition of edit distance tree codes is a main conceptual
contribution of this work
Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions
We present many new results related to reliable (interactive) communication over insertion-deletion channels. Synchronization errors, such as insertions and deletions, strictly generalize the usual symbol corruption errors and are much harder to protect against.
We show how to hide the complications of synchronization errors in many applications by introducing very general channel simulations which efficiently transform an insertion-deletion channel into a regular symbol corruption channel with an error rate larger by a constant factor and a slightly smaller alphabet. We utilize and generalize synchronization string based methods which were recently introduced as a tool to design essentially optimal error correcting codes for insertion-deletion channels. Our channel simulations depend on the fact that, at the cost of increasing the error rate by a constant factor, synchronization strings can be decoded in a streaming manner that preserves linearity of time. Interestingly, we provide a lower bound showing that this constant factor cannot be improved to 1+epsilon, in contrast to what is achievable for error correcting codes. Our channel simulations drastically and cleanly generalize the applicability of synchronization strings.
We provide new interactive coding schemes which simulate any interactive two-party protocol over an insertion-deletion channel. Our results improve over the interactive coding schemes of Braverman et al. [TransInf `17] and Sherstov and Wu [FOCS `17] which achieve a small constant rate and require exponential time computations with respect to computational and communication complexities. We provide the first computationally efficient interactive coding schemes for synchronization errors, the first coding scheme with a rate approaching one for small noise rates, and also the first coding scheme that works over arbitrarily small alphabet sizes. We also show tight connections between synchronization strings and edit-distance tree codes which allow us to transfer results from tree codes directly to edit-distance tree codes.
Finally, using on our channel simulations, we provide an explicit low-rate binary insertion-deletion code that improves over the state-of-the-art codes by Guruswami and Wang [TransInf `17] in terms of rate-distance trade-off
Doing expertise multilingually and multimodally in online English teaching videos
The use of online videos as a teaching resource is gaining importance. It opens up opportunities for the creation of knowledge, as educational content can now be accessed by anyone with an Internet connection. This democratisation of access to knowledge can also be seen in the language learning context, where English language teachers create online videos for a transnational audience. In this paper, we present a case study of how two online English teachers ‘do expertise’ in their lessons, drawing on their multilingual and multimodal repertoire so that expertise is talked into being. We conducted semi-structured interviews and analysed them by interpretative phenomenological analysis (IPA) to make sense of the teachers’ experiences of designing online teaching materials, and how their expertise was talked into being in the process. The aim of this paper is to contribute to understanding of expertise in the context of online language teaching. We argue that online teachers ‘do expertise’ by drawing on their multimodal design knowledge, pedagogical knowledge and linguistic knowledge
Control, Confidentiality, and the Right to be Forgotten
Recent digital rights frameworks give users the right to delete their data
from systems that store and process their personal information (e.g., the
"right to be forgotten" in the GDPR). How should deletion be formalized in
complex systems that interact with many users and store derivative information?
We argue that prior approaches fall short. Definitions of machine unlearning
Cao and Yang [2015] are too narrowly scoped and do not apply to general
interactive settings. The natural approach of deletion-as-confidentiality Garg
et al. [2020] is too restrictive: by requiring secrecy of deleted data, it
rules out social functionalities. We propose a new formalism:
deletion-as-control. It allows users' data to be freely used before deletion,
while also imposing a meaningful requirement after deletion--thereby giving
users more control. Deletion-as-control provides new ways of achieving deletion
in diverse settings. We apply it to social functionalities, and give a new
unified view of various machine unlearning definitions from the literature.
This is done by way of a new adaptive generalization of history independence.
Deletion-as-control also provides a new approach to the goal of machine
unlearning, that is, to maintaining a model while honoring users' deletion
requests. We show that publishing a sequence of updated models that are
differentially private under continual release satisfies deletion-as-control.
The accuracy of such an algorithm does not depend on the number of deleted
points, in contrast to the machine unlearning literature
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