25 research outputs found
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems
A growing number of applications, e.g. video surveillance and medical image
analysis, require training recognition systems from large amounts of weakly
annotated data while some targeted interactions with a domain expert are
allowed to improve the training process. In such cases, active learning (AL)
can reduce labeling costs for training a classifier by querying the expert to
provide the labels of most informative instances. This paper focuses on AL
methods for instance classification problems in multiple instance learning
(MIL), where data is arranged into sets, called bags, that are weakly labeled.
Most AL methods focus on single instance learning problems. These methods are
not suitable for MIL problems because they cannot account for the bag structure
of data. In this paper, new methods for bag-level aggregation of instance
informativeness are proposed for multiple instance active learning (MIAL). The
\textit{aggregated informativeness} method identifies the most informative
instances based on classifier uncertainty, and queries bags incorporating the
most information. The other proposed method, called \textit{cluster-based
aggregative sampling}, clusters data hierarchically in the instance space. The
informativeness of instances is assessed by considering bag labels, inferred
instance labels, and the proportion of labels that remain to be discovered in
clusters. Both proposed methods significantly outperform reference methods in
extensive experiments using benchmark data from several application domains.
Results indicate that using an appropriate strategy to address MIAL problems
yields a significant reduction in the number of queries needed to achieve the
same level of performance as single instance AL methods
Feature Learning from Spectrograms for Assessment of Personality Traits
Several methods have recently been proposed to analyze speech and
automatically infer the personality of the speaker. These methods often rely on
prosodic and other hand crafted speech processing features extracted with
off-the-shelf toolboxes. To achieve high accuracy, numerous features are
typically extracted using complex and highly parameterized algorithms. In this
paper, a new method based on feature learning and spectrogram analysis is
proposed to simplify the feature extraction process while maintaining a high
level of accuracy. The proposed method learns a dictionary of discriminant
features from patches extracted in the spectrogram representations of training
speech segments. Each speech segment is then encoded using the dictionary, and
the resulting feature set is used to perform classification of personality
traits. Experiments indicate that the proposed method achieves state-of-the-art
results with a significant reduction in complexity when compared to the most
recent reference methods. The number of features, and difficulties linked to
the feature extraction process are greatly reduced as only one type of
descriptors is used, for which the 6 parameters can be tuned automatically. In
contrast, the simplest reference method uses 4 types of descriptors to which 6
functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
Génération et synchronisation des horloges pour un système micro-ondes 1024 QAM
Un récepteur QAM doit échantillonner le signal modulé reçu une fois par symbole. L'échantillonnage doit se faire au moment idéal, au centre de "l'oeil". Lorsque du bruit de phase corrompt les horloges à l'émetteur et au récepteur, l'oeil apparaît plus fermé, dégradant les performances théoriques du système de communication. Les systèmes QAM à haut niveau sont très sensibles au bruit de phase sur les horloges.
Ce mémoire propose un système de génération et synchronisation des horloges adapté aux communications QAM à haut niveau, minimisant le bruit de phase. Le système est basé sur deux boucles à verrouillage de phase numériques. La boucle à l'émetteur contient un diviseur de fréquence fractionnaire par conversion sigma-delta afin d'obtenir une bonne résolution de la fréquence en sortie sans sacrifier la largeur de bande de la boucle. La boucle au récepteur comporte un préfiltre numérique de mise en forme du signal minimisant le bruit de phase généré par la boucle.
Le circuit de génération d'horloge a été entièrement réalisé sur une plateforme de développement avec FPGA. Le spectre théorique du bruit de phase de ce circuit est d'abord calculé en prenant en considération chaque source de bruit. Les calculs théoriques sont comparés aux spectres relevés en pratique, confirmant la validité des équations développées dans ce travail
Early Detection for Optimal-Latency Communications in Multi-Hop Links
Modern wireless machine-to-machine-type communications aim to provide both
ultra reliability and low latency, stringent requirements that appear to be
mutually exclusive. From the noisy channel coding theorem, we know that
reliable communications mandate transmission rates that are lower than the
channel capacity. To guarantee arbitrarily-low error probability, this implies
the use of messages whose lengths tend to infinity. However, long messages are
not suitable for low-latency communications. In this paper, we propose an
early-detection scheme for wireless communications under a finite-blocklength
regime that employs a sequential-test technique to reduce latency while
maintaining reliability. We prove that our scheme leads to an average detection
time smaller than the symbol duration. Furthermore, in multi-hop low-traffic or
continuous-transmission links, we show that our scheme can reliably detect
symbols before the end of their transmission, significantly reducing the
latency, while keeping the error probability below a predefined threshold.Comment: 6 pages, to be presented at the International Symposium on Wireless
Communication Systems (ISWCS) 2019; Fixed some reference
Improvement Of Audiovisual Quality Estimation Using A Nonlinear Autoregressive Exogenous Neural Network And Bitstream Parameters
With the increasing demand for audiovisual services, telecom service
providers and application developers are compelled to ensure that their
services provide the best possible user experience. Particularly, services such
as videoconferencing are very sensitive to network conditions. Therefore, their
performance should be monitored in real time in order to adjust parameters to
any network perturbation. In this paper, we developed a parametric model for
estimating the perceived audiovisual quality in videoconference services. Our
model is developed with the nonlinear autoregressive exogenous (NARX) recurrent
neural network and estimates the perceived quality in terms of mean opinion
score (MOS). We validate our model using the publicly available INRS bitstream
audiovisual quality dataset. This dataset contains bitstream parameters such as
loss per frame, bit rate and video duration. We compare the proposed model
against state-of-the-art methods based on machine learning and show our model
to outperform these methods in terms of mean square error (MSE=0.150) and
Pearson correlation coefficient (R=0.931
Measuring Disentanglement: A Review of Metrics
Learning to disentangle and represent factors of variation in data is an
important problem in AI. While many advances are made to learn these
representations, it is still unclear how to quantify disentanglement. Several
metrics exist, however little is known on their implicit assumptions, what they
truly measure and their limits. As a result, it is difficult to interpret
results when comparing different representations. In this work, we survey
supervised disentanglement metrics and thoroughly analyze them. We propose a
new taxonomy in which all metrics fall into one of three families:
intervention-based, predictor-based and information-based. We conduct extensive
experiments, where we isolate representation properties to compare all metrics
on many aspects. From experiment results and analysis, we provide insights on
relations between disentangled representation properties. Finally, we provide
guidelines on how to measure disentanglement and report the results