417,694 research outputs found
User-Relative Names for Globally Connected Personal Devices
Nontechnical users who own increasingly ubiquitous network-enabled personal
devices such as laptops, digital cameras, and smart phones need a simple,
intuitive, and secure way to share information and services between their
devices. User Information Architecture, or UIA, is a novel naming and
peer-to-peer connectivity architecture addressing this need. Users assign UIA
names by "introducing" devices to each other on a common local-area network,
but these names remain securely bound to their target as devices migrate.
Multiple devices owned by the same user, once introduced, automatically merge
their namespaces to form a distributed "personal cluster" that the owner can
access or modify from any of his devices. Instead of requiring users to
allocate globally unique names from a central authority, UIA enables users to
assign their own "user-relative" names both to their own devices and to other
users. With UIA, for example, Alice can always access her iPod from any of her
own personal devices at any location via the name "ipod", and her friend Bob
can access her iPod via a relative name like "ipod.Alice".Comment: 7 pages, 1 figure, 1 tabl
Enabling DVFS Side-Channel Attacks for Neural Network Fingerprinting in Edge Inference Services
The Inference-as-a-Service (IaaS) delivery model provides users access to pre-trained deep neural networks while safeguarding network code and weights. However, IaaS is not immune to security threats, like side-channel attacks (SCAs), that exploit unintended information leakage from the physical characteristics of the target device. Exposure to such threats grows when IaaS is deployed on distributed computing nodes at the edge. This work identifies a potential vulnerability of low-power CPUs that facilitates stealing the deep neural network architecture without physical access to the hardware or interference with the execution flow. Our approach relies on a Dynamic Voltage and Frequency Scaling (DVFS) side-channel attack, which monitors the CPU frequency state during the inference stages. Specifically, we introduce a dedicated load-testing methodology that imprints distinguishable signatures of the network on the frequency traces. A machine learning classifier is then used to infer the victim architecture. Experimental results on two commercial ARM Cortex-A CPUs, the A72 and A57, demonstrate the attack can identify the target architecture from a pool of 12 convolutional neural networks with an average accuracy of 98.7% and 92.4
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Exact Learning with Tunable Quantum Neural Networks and a Quantum Example Oracle
In this paper, we study the tunable quantum neural network architecture in
the quantum exact learning framework with access to a uniform quantum example
oracle. We present an approach that uses amplitude amplification to correctly
tune the network to the target concept. We applied our approach to the class of
positive -juntas and found that quantum examples are sufficient
with experimental results seemingly showing that a tighter upper bound is
possible
Multi-component Image Translation for Deep Domain Generalization
Domain adaption (DA) and domain generalization (DG) are two closely related
methods which are both concerned with the task of assigning labels to an
unlabeled data set. The only dissimilarity between these approaches is that DA
can access the target data during the training phase, while the target data is
totally unseen during the training phase in DG. The task of DG is challenging
as we have no earlier knowledge of the target samples. If DA methods are
applied directly to DG by a simple exclusion of the target data from training,
poor performance will result for a given task. In this paper, we tackle the
domain generalization challenge in two ways. In our first approach, we propose
a novel deep domain generalization architecture utilizing synthetic data
generated by a Generative Adversarial Network (GAN). The discrepancy between
the generated images and synthetic images is minimized using existing domain
discrepancy metrics such as maximum mean discrepancy or correlation alignment.
In our second approach, we introduce a protocol for applying DA methods to a DG
scenario by excluding the target data from the training phase, splitting the
source data to training and validation parts, and treating the validation data
as target data for DA. We conduct extensive experiments on four cross-domain
benchmark datasets. Experimental results signify our proposed model outperforms
the current state-of-the-art methods for DG.Comment: Accepted in WACV 201
Inviwo -- A Visualization System with Usage Abstraction Levels
The complexity of today's visualization applications demands specific
visualization systems tailored for the development of these applications.
Frequently, such systems utilize levels of abstraction to improve the
application development process, for instance by providing a data flow network
editor. Unfortunately, these abstractions result in several issues, which need
to be circumvented through an abstraction-centered system design. Often, a high
level of abstraction hides low level details, which makes it difficult to
directly access the underlying computing platform, which would be important to
achieve an optimal performance. Therefore, we propose a layer structure
developed for modern and sustainable visualization systems allowing developers
to interact with all contained abstraction levels. We refer to this interaction
capabilities as usage abstraction levels, since we target application
developers with various levels of experience. We formulate the requirements for
such a system, derive the desired architecture, and present how the concepts
have been exemplary realized within the Inviwo visualization system.
Furthermore, we address several specific challenges that arise during the
realization of such a layered architecture, such as communication between
different computing platforms, performance centered encapsulation, as well as
layer-independent development by supporting cross layer documentation and
debugging capabilities
Transfer Learning with Pre-trained Conditional Generative Models
Transfer learning is crucial in training deep neural networks on new target
tasks. Current transfer learning methods always assume at least one of (i)
source and target task label spaces overlap, (ii) source datasets are
available, and (iii) target network architectures are consistent with source
ones. However, holding these assumptions is difficult in practical settings
because the target task rarely has the same labels as the source task, the
source dataset access is restricted due to storage costs and privacy, and the
target architecture is often specialized to each task. To transfer source
knowledge without these assumptions, we propose a transfer learning method that
uses deep generative models and is composed of the following two stages: pseudo
pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a
target architecture with an artificial dataset synthesized by using conditional
source generative models. P-SSL applies SSL algorithms to labeled target data
and unlabeled pseudo samples, which are generated by cascading the source
classifier and generative models to condition them with target samples. Our
experimental results indicate that our method can outperform the baselines of
scratch training and knowledge distillation.Comment: 24 pages, 6 figure
- …