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Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Low-Complexity Audio Embedding Extractors
Solving tasks such as speaker recognition, music classification, or semantic
audio event tagging with deep learning models typically requires
computationally demanding networks. General-purpose audio embeddings (GPAEs)
are dense representations of audio signals that allow lightweight, shallow
classifiers to tackle various audio tasks. The idea is that a single complex
feature extractor would extract dense GPAEs, while shallow MLPs can produce
task-specific predictions. If the extracted dense representations are general
enough to allow the simple downstream classifiers to generalize to a variety of
tasks in the audio domain, a single costly forward pass suffices to solve
multiple tasks in parallel. In this work, we try to reduce the cost of GPAE
extractors to make them suitable for resource-constrained devices. We use
efficient MobileNets trained on AudioSet using Knowledge Distillation from a
Transformer ensemble as efficient GPAE extractors. We explore how to obtain
high-quality GPAEs from the model, study how model complexity relates to the
quality of extracted GPAEs, and conclude that low-complexity models can
generate competitive GPAEs, paving the way for analyzing audio streams on edge
devices w.r.t. multiple audio classification and recognition tasks.Comment: In Proceedings of the 31st European Signal Processing Conference,
EUSIPCO 2023. Source Code available at:
https://github.com/fschmid56/EfficientAT_HEA
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth
of data that cannot be accumulated in a centralized repository for learning
supervised models due to privacy, bandwidth limitations, and the prohibitive
cost of annotations. Federated learning provides a compelling framework for
learning models from decentralized data, but conventionally, it assumes the
availability of labeled samples, whereas on-device data are generally either
unlabeled or cannot be annotated readily through user interaction. To address
these issues, we propose a self-supervised approach termed
\textit{scalogram-signal correspondence learning} based on wavelet transform to
learn useful representations from unlabeled sensor inputs, such as
electroencephalography, blood volume pulse, accelerometer, and WiFi channel
state information. Our auxiliary task requires a deep temporal neural network
to determine if a given pair of a signal and its complementary viewpoint (i.e.,
a scalogram generated with a wavelet transform) align with each other or not
through optimizing a contrastive objective. We extensively assess the quality
of learned features with our multi-view strategy on diverse public datasets,
achieving strong performance in all domains. We demonstrate the effectiveness
of representations learned from an unlabeled input collection on downstream
tasks with training a linear classifier over pretrained network, usefulness in
low-data regime, transfer learning, and cross-validation. Our methodology
achieves competitive performance with fully-supervised networks, and it
outperforms pre-training with autoencoders in both central and federated
contexts. Notably, it improves the generalization in a semi-supervised setting
as it reduces the volume of labeled data required through leveraging
self-supervised learning.Comment: Accepted for publication at IEEE Internet of Things Journa
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