2,579 research outputs found
Flat Holonomies on Automata Networks
We consider asynchronous networks of identical finite (independent of
network's size or topology) automata. Our automata drive any network from any
initial configuration of states, to a coherent one in which it can carry
efficiently any computations implementable on synchronous properly initialized
networks of the same size.
A useful data structure on such networks is a partial orientation of its
edges. It needs to be flat, i.e. have null holonomy (no excess of up or down
edges in any cycle). It also needs to be centered, i.e. have a unique node with
no down edges.
There are (interdependent) self-stabilizing asynchronous finite automata
protocols assuring flat centered orientation. Such protocols may vary in
assorted efficiency parameters and it is desirable to have each replaceable
with any alternative, responsible for a simple limited task. We describe an
efficient reduction of any computational task to any such set of protocols
compliant with our interface conditions.Comment: 20 pages, significant revisio
Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space
Approximate nearest neighbour (ANN) search is one of the most important
problems in computer science fields such as data mining or computer vision. In
this paper, we focus on ANN for high-dimensional binary vectors and we propose
a simple yet powerful search method that uses Random Binary Search Trees
(RBST). We apply our method to a dataset of 1.25M binary local feature
descriptors obtained from a real-life image-based localisation system provided
by Google as a part of Project Tango. An extensive evaluation of our method
against the state-of-the-art variations of Locality Sensitive Hashing (LSH),
namely Uniform LSH and Multi-probe LSH, shows the superiority of our method in
terms of retrieval precision with performance boost of over 20%Comment: The final publication is available at Springer via
https://doi.org/10.1007/978-3-319-69900-4_6
Towards balanced clustering - part 1 (preliminaries)
The article contains a preliminary glance at balanced clustering problems.
Basic balanced structures and combinatorial balanced problems are briefly
described. A special attention is targeted to various balance/unbalance indices
(including some new versions of the indices): by cluster cardinality, by
cluster weights, by inter-cluster edge/arc weights, by cluster element
structure (for element multi-type clustering). Further, versions of
optimization clustering problems are suggested (including multicriteria problem
formulations). Illustrative numerical examples describe calculation of balance
indices and element multi-type balance clustering problems (including example
for design of student teams).Comment: 21 pages, 17 figures, 14 table
The Pyramid Scheme: Oblivious RAM for Trusted Processors
Modern processors, e.g., Intel SGX, allow applications to isolate secret code
and data in encrypted memory regions called enclaves. While encryption
effectively hides the contents of memory, the sequence of address references
issued by the secret code leaks information. This is a serious problem because
these leaks can easily break the confidentiality guarantees of enclaves.
In this paper, we explore Oblivious RAM (ORAM) designs that prevent these
information leaks under the constraints of modern SGX processors. Most ORAMs
are a poor fit for these processors because they have high constant overhead
factors or require large private memories, which are not available in these
processors. We address these limitations with a new hierarchical ORAM
construction, the Pyramid ORAM, that is optimized towards online bandwidth cost
and small blocks. It uses a new hashing scheme that circumvents the complexity
of previous hierarchical schemes.
We present an efficient x64-optimized implementation of Pyramid ORAM that
uses only the processor's registers as private memory. We compare Pyramid ORAM
with Circuit ORAM, a state-of-the-art tree-based ORAM scheme that also uses
constant private memory. Pyramid ORAM has better online asymptotical complexity
than Circuit ORAM. Our implementation of Pyramid ORAM and Circuit ORAM
validates this: as all hierarchical schemes, Pyramid ORAM has high variance of
access latencies; although latency can be high for some accesses, for typical
configurations Pyramid ORAM provides access latencies that are 8X better than
Circuit ORAM for 99% of accesses. Although the best known hierarchical ORAM has
better asymptotical complexity, Pyramid ORAM has significantly lower constant
overhead factors, making it the preferred choice in practice
Learning Decision Trees Recurrently Through Communication
Integrated interpretability without sacrificing the prediction accuracy of
decision making algorithms has the potential of greatly improving their value
to the user. Instead of assigning a label to an image directly, we propose to
learn iterative binary sub-decisions, inducing sparsity and transparency in the
decision making process. The key aspect of our model is its ability to build a
decision tree whose structure is encoded into the memory representation of a
Recurrent Neural Network jointly learned by two models communicating through
message passing. In addition, our model assigns a semantic meaning to each
decision in the form of binary attributes, providing concise, semantic and
relevant rationalizations to the user. On three benchmark image classification
datasets, including the large-scale ImageNet, our model generates human
interpretable binary decision sequences explaining the predictions of the
network while maintaining state-of-the-art accuracy.Comment: Accepted in IEEE CVPR 202
Exploring Connections Between Active Learning and Model Extraction
Machine learning is being increasingly used by individuals, research
institutions, and corporations. This has resulted in the surge of Machine
Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and
resources to learn the model, and (b) a user-friendly query interface to access
the model. However, such MLaaS systems raise privacy concerns such as model
extraction. In model extraction attacks, adversaries maliciously exploit the
query interface to steal the model. More precisely, in a model extraction
attack, a good approximation of a sensitive or proprietary model held by the
server is extracted (i.e. learned) by a dishonest user who interacts with the
server only via the query interface. This attack was introduced by Tramer et
al. at the 2016 USENIX Security Symposium, where practical attacks for various
models were shown. We believe that better understanding the efficacy of model
extraction attacks is paramount to designing secure MLaaS systems. To that end,
we take the first step by (a) formalizing model extraction and discussing
possible defense strategies, and (b) drawing parallels between model extraction
and established area of active learning. In particular, we show that recent
advancements in the active learning domain can be used to implement powerful
model extraction attacks, and investigate possible defense strategies
Instance and Output Optimal Parallel Algorithms for Acyclic Joins
Massively parallel join algorithms have received much attention in recent
years, while most prior work has focused on worst-optimal algorithms. However,
the worst-case optimality of these join algorithms relies on hard instances
having very large output sizes, which rarely appear in practice. A stronger
notion of optimality is {\em output-optimal}, which requires an algorithm to be
optimal within the class of all instances sharing the same input and output
size. An even stronger optimality is {\em instance-optimal}, i.e., the
algorithm is optimal on every single instance, but this may not always be
achievable.
In the traditional RAM model of computation, the classical Yannakakis
algorithm is instance-optimal on any acyclic join. But in the massively
parallel computation (MPC) model, the situation becomes much more complicated.
We first show that for the class of r-hierarchical joins, instance-optimality
can still be achieved in the MPC model. Then, we give a new MPC algorithm for
an arbitrary acyclic join with load O ({\IN \over p} + {\sqrt{\IN \cdot \OUT}
\over p}), where \IN,\OUT are the input and output sizes of the join, and
is the number of servers in the MPC model. This improves the MPC version of
the Yannakakis algorithm by an O (\sqrt{\OUT \over \IN} ) factor.
Furthermore, we show that this is output-optimal when \OUT = O(p \cdot \IN),
for every acyclic but non-r-hierarchical join. Finally, we give the first
output-sensitive lower bound for the triangle join in the MPC model, showing
that it is inherently more difficult than acyclic joins
A Survey and Evaluation of Data Center Network Topologies
Data centers are becoming increasingly popular for their flexibility and
processing capabilities in the modern computing environment. They are managed
by a single entity (administrator) and allow dynamic resource provisioning,
performance optimization as well as efficient utilization of available
resources. Each data center consists of massive compute, network and storage
resources connected with physical wires. The large scale nature of data centers
requires careful planning of compute, storage, network nodes, interconnection
as well as inter-communication for their effective and efficient operations. In
this paper, we present a comprehensive survey and taxonomy of network
topologies either used in commercial data centers, or proposed by researchers
working in this space. We also compare and evaluate some of those topologies
using mininet as well as gem5 simulator for different traffic patterns, based
on various metrics including throughput, latency and bisection bandwidth
Energy-aware Allocation of Graph Jobs in Vehicular Cloud Computing-enabled Software-defined IoV
Software-defined internet of vehicles (SDIoV) has emerged as a promising
paradigm to realize flexible and comprehensive resource management, for next
generation automobile transportation systems. In this paper, a vehicular cloud
computing-based SDIoV framework is studied wherein the joint allocation of
transmission power and graph job is formulated as a nonlinear integer
programming problem. To effectively address the problem, a
structure-preservation-based two-stage allocation scheme is proposed that
decouples template searching from power allocation. Specifically, a
hierarchical tree-based random subgraph isomorphism mechanism is applied in the
first stage by identifying potential mappings (templates) between the
components of graph jobs and service providers. A structure-preserving
simulated annealing-based power allocation algorithm is adopted in the second
stage to achieve the trade-off between the job completion time and energy
consumption. Extensive simulations are conducted to verify the performance of
the proposed algorithms.Comment: 6 pages, 4 figures, INFOCOM WORKSHOP 202
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