25,573 research outputs found
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Understanding predictions made by deep neural networks is notoriously
difficult, but also crucial to their dissemination. As all ML-based methods,
they are as good as their training data, and can also capture unwanted biases.
While there are tools that can help understand whether such biases exist, they
do not distinguish between correlation and causation, and might be ill-suited
for text-based models and for reasoning about high level language concepts. A
key problem of estimating the causal effect of a concept of interest on a given
model is that this estimation requires the generation of counterfactual
examples, which is challenging with existing generation technology. To bridge
that gap, we propose CausaLM, a framework for producing causal model
explanations using counterfactual language representation models. Our approach
is based on fine-tuning of deep contextualized embedding models with auxiliary
adversarial tasks derived from the causal graph of the problem. Concretely, we
show that by carefully choosing auxiliary adversarial pre-training tasks,
language representation models such as BERT can effectively learn a
counterfactual representation for a given concept of interest, and be used to
estimate its true causal effect on model performance. A byproduct of our method
is a language representation model that is unaffected by the tested concept,
which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at:
https://amirfeder.github.io/CausaLM/ Under review for the Computational
Linguistics journa
On the Modeling of Musical Solos as Complex Networks
Notes in a musical piece are building blocks employed in non-random ways to
create melodies. It is the "interaction" among a limited amount of notes that
allows constructing the variety of musical compositions that have been written
in centuries and within different cultures. Networks are a modeling tool that
is commonly employed to represent a set of entities interacting in some way.
Thus, notes composing a melody can be seen as nodes of a network that are
connected whenever these are played in sequence. The outcome of such a process
results in a directed graph. By using complex network theory, some main metrics
of musical graphs can be measured, which characterize the related musical
pieces. In this paper, we define a framework to represent melodies as networks.
Then, we provide an analysis on a set of guitar solos performed by main
musicians. Results of this study indicate that the presented model can have an
impact on audio and multimedia applications such as music classification,
identification, e-learning, automatic music generation, multimedia
entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper
including such information. arXiv admin note: text overlap with
arXiv:1603.0497
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control
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