90 research outputs found
Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey
Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications
Clustering-based Source-aware Assessment of True Robustness for Learning Models
We introduce a novel validation framework to measure the true robustness of
learning models for real-world applications by creating source-inclusive and
source-exclusive partitions in a dataset via clustering. We develop a
robustness metric derived from source-aware lower and upper bounds of model
accuracy even when data source labels are not readily available. We clearly
demonstrate that even on a well-explored dataset like MNIST, challenging
training scenarios can be constructed under the proposed assessment framework
for two separate yet equally important applications: i) more rigorous learning
model comparison and ii) dataset adequacy evaluation. In addition, our findings
not only promise a more complete identification of trade-offs between model
complexity, accuracy and robustness but can also help researchers optimize
their efforts in data collection by identifying the less robust and more
challenging class labels.Comment: Submitted to UAI 201
Active Learning for One-Class Classification Using Two One-Class Classifiers
This paper introduces a novel, generic active learning method for one-class
classification. Active learning methods play an important role to reduce the
efforts of manual labeling in the field of machine learning. Although many
active learning approaches have been proposed during the last years, most of
them are restricted on binary or multi-class problems. One-class classifiers
use samples from only one class, the so-called target class, during training
and hence require special active learning strategies. The few strategies
proposed for one-class classification either suffer from their limitation on
specific one-class classifiers or their performance depends on particular
assumptions about datasets like imbalance. Our proposed method bases on using
two one-class classifiers, one for the desired target class and one for the
so-called outlier class. It allows to invent new query strategies, to use
binary query strategies and to define simple stopping criteria. Based on the
new method, two query strategies are proposed. The provided experiments compare
the proposed approach with known strategies on various datasets and show
improved results in almost all situations.Comment: EUSIPCO 201
Regularized Data Programming with Automated Bayesian Prior Selection
The cost of manual data labeling can be a significant obstacle in supervised
learning. Data programming (DP) offers a weakly supervised solution for
training dataset creation, wherein the outputs of user-defined programmatic
labeling functions (LFs) are reconciled through unsupervised learning. However,
DP can fail to outperform an unweighted majority vote in some scenarios,
including low-data contexts. This work introduces a Bayesian extension of
classical DP that mitigates failures of unsupervised learning by augmenting the
DP objective with regularization terms. Regularized learning is achieved
through maximum a posteriori estimation with informative priors. Majority vote
is proposed as a proxy signal for automated prior parameter selection. Results
suggest that regularized DP improves performance relative to maximum likelihood
and majority voting, confers greater interpretability, and bolsters performance
in low-data regimes
Anomaly detection of consumption in Hotel Units: A case study comparing isolation forest and variational autoencoder algorithms
Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.info:eu-repo/semantics/publishedVersio
Clustering Financial Time Series: How Long is Enough?
Researchers have used from 30 days to several years of daily returns as
source data for clustering financial time series based on their correlations.
This paper sets up a statistical framework to study the validity of such
practices. We first show that clustering correlated random variables from their
observed values is statistically consistent. Then, we also give a first
empirical answer to the much debated question: How long should the time series
be? If too short, the clusters found can be spurious; if too long, dynamics can
be smoothed out.Comment: Accepted at IJCAI 201
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Inspired by the success of deploying deep learning in the fields of Computer
Vision and Natural Language Processing, this learning paradigm has also found
its way into the field of Music Information Retrieval. In order to benefit from
deep learning in an effective, but also efficient manner, deep transfer
learning has become a common approach. In this approach, it is possible to
reuse the output of a pre-trained neural network as the basis for a new
learning task. The underlying hypothesis is that if the initial and new
learning tasks show commonalities and are applied to the same type of input
data (e.g. music audio), the generated deep representation of the data is also
informative for the new task. Since, however, most of the networks used to
generate deep representations are trained using a single initial learning
source, their representation is unlikely to be informative for all possible
future tasks. In this paper, we present the results of our investigation of
what are the most important factors to generate deep representations for the
data and learning tasks in the music domain. We conducted this investigation
via an extensive empirical study that involves multiple learning sources, as
well as multiple deep learning architectures with varying levels of information
sharing between sources, in order to learn music representations. We then
validate these representations considering multiple target datasets for
evaluation. The results of our experiments yield several insights on how to
approach the design of methods for learning widely deployable deep data
representations in the music domain.Comment: This work has been accepted to "Neural Computing and Applications:
Special Issue on Deep Learning for Music and Audio
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel
Recent Deep Learning (DL) advancements in solving complex real-world tasks
have led to its widespread adoption in practical applications. However, this
opportunity comes with significant underlying risks, as many of these models
rely on privacy-sensitive data for training in a variety of applications,
making them an overly-exposed threat surface for privacy violations.
Furthermore, the widespread use of cloud-based Machine-Learning-as-a-Service
(MLaaS) for its robust infrastructure support has broadened the threat surface
to include a variety of remote side-channel attacks. In this paper, we first
identify and report a novel data-dependent timing side-channel leakage (termed
Class Leakage) in DL implementations originating from non-constant time
branching operation in a widely used DL framework PyTorch. We further
demonstrate a practical inference-time attack where an adversary with user
privilege and hard-label black-box access to an MLaaS can exploit Class Leakage
to compromise the privacy of MLaaS users. DL models are vulnerable to
Membership Inference Attack (MIA), where an adversary's objective is to deduce
whether any particular data has been used while training the model. In this
paper, as a separate case study, we demonstrate that a DL model secured with
differential privacy (a popular countermeasure against MIA) is still vulnerable
to MIA against an adversary exploiting Class Leakage. We develop an
easy-to-implement countermeasure by making a constant-time branching operation
that alleviates the Class Leakage and also aids in mitigating MIA. We have
chosen two standard benchmarking image classification datasets, CIFAR-10 and
CIFAR-100 to train five state-of-the-art pre-trained DL models, over two
different computing environments having Intel Xeon and Intel i7 processors to
validate our approach.Comment: 15 pages, 20 figure
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