725 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Latent Class Model with Application to Speaker Diarization
In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides a framework allowing both generative and discriminative models.
The discriminative property is realized through the use of i-vector (Ivec),
probabilistic linear discriminative analysis (PLDA), and a support vector
machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid are introduced. In addition, three further improvements are
applied to enhance its performance. 1) Adding neighbor windows to extract more
speaker information for each short segment. 2) Using a hidden Markov model to
avoid frequent speaker change points. 3) Using an agglomerative hierarchical
cluster to do initialization and present hard and soft priors, in order to
overcome the problem of initial sensitivity. Experiments on the National
Institute of Standards and Technology Rich Transcription 2009 speaker
diarization database, under the condition of a single distant microphone, show
that the diarization error rate (DER) of the proposed methods has substantial
relative improvements compared with mainstream systems. Compared to the VB
method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments
on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial
conditions also show that the proposed LCM-Ivec-Hybrid system has the best
overall performance
Grounding of Human Environments and Activities for Autonomous Robots
With the recent proliferation of robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a framework for autonomous, unsupervised learning from various sensory sources of useful human ‘concepts’; including colours, people names, usable objects and simple activities. This is achieved by integrating state-of-the-art object segmentation, pose estimation, activity analysis and language grounding into a continual learning framework. Learned concepts are grounded to natural language if commentary is available, allowing the robot to communicate in a human-understandable way. We show, using a challenging, real-world dataset of human activities, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-of-concept, to generate simple sentences from templates to describe people and activities
Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short Term Memory and Autoencoder
Currently, the wide spreading of real-time applications such as VoIP and
videos-based applications require more data rates and reduced latency to ensure
better quality of service (QoS). A well-designed traffic classification
mechanism plays a major role for good QoS provision and network security
verification. Port-based approaches and deep packet inspections (DPI)
techniques have been used to classify and analyze network traffic flows.
However, none of these methods can cope with the rapid growth of network
traffic due to the increasing number of Internet users and the growth of real
time applications. As a result, these methods lead to network congestion,
resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep
learning approach has been explored to address the time-consumption and
impracticality gaps of the above methods and maintain existing and future
traffics of real-time applications. The aim of this research is then to design
a dynamic traffic classifier that can detect elephant flows to prevent network
congestion. Thus, we are motivated to provide efficient bandwidth and fast
transmision requirements to many Internet users using SDN capability and the
potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder
are used to build elephant detection models that achieve an average accuracy of
99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the
promising algorithms that does not require human class labeler. It achieves an
accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to
zero, the performance of the model is good. Therefore, the study has a great
importance to Internet service providers, Internet subscribers, as well as for
future researchers in this area.Comment: 27 page
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