Italy


Dealing with opioid addiction and its long-term
consequences is of great importance, as the addiction to opioids is
emerged gradually, and established strongly in a given patient’s
body. Based on recent research, quitting the opioid requires
clinicians to arrange a gradual plan for the patients who deal
with the difficulties of overcoming addiction. This, in turn,
necessitates observing the patients’ wellness periodically, which
is conventionally made by setting clinical appointments. With
the advent of wearable sensors continuous patient monitoring
becomes possible. However, the data collected through the sensors
is pervasively noisy, where using sensors with different sampling
frequency challenges the data processing. In this work, we handle
this problem by using data from cognitive tests, along with heart
rate (HR) and heart rate variability (HRV). The proposed recipe
enables us to interpret the data as a feature space, where we can
predict the wellness of the opioid patients by employing extreme
gradient boosting (XGBoost), which results in 96.12% average
accuracy of prediction as the best-achieved performance.

ICPR 2020

XGBoost to Interpret the Opioid Patients’ State Based on Cognitive and Physiological Measures

xgboost predictive modeling

wearable monitoring

physiology

well-being state

opioid

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Video Prediction is a challenging but interesting task of predicting future frames from a given set context frames that belong to a video sequence. Video prediction models have prospective applications in maneuver planning, healthcare, autonomous navigation and simulation. One of the major challenges in future frame generation is the high dimensional nature of visual data. To handle this, we propose a Mutual Information Predictive Auto-Encoder (MIPAE) framework that reduces the task of predicting high dimensional video frames by factorising video representations into content and low dimensional pose latent variables. Our approach leverages the temporal structure in the latent generative factors of video sequences by applying a novel mutual information loss to learn disentangled video representations. A standard LSTM network is used to predict these low dimensional pose representations. Content and the predicted pose representations are decoded to generate future frames. We also propose a metric based on mutual information gap (MIG) to quantitatively access the effectiveness of disentanglement on DSprites and MPI3D-real datasets. MIG scores corroborate the visual superiority of frames
predicted by MIPAE. We also compare our method quantitatively on LPIPS, SSIM and PSNR evaluation metrics.

Mutual Information based Method for Unsupervised Disentanglement of Video Representation


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

Energy Minimum Regularization in Continual Learning

Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose
an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions.
Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

Attentive Hybrid Feature with Two-Step Fusion forFacial Expression Recognition

Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks
(ANNs). We build an information theory-inspired system called Stochastic Probability Adjustment (SPA) system to reduce this gap. The SPA maps the synapses and neurons of SNNs into a probability space where a neuron and all connected pre-synapses are represented by a cluster. The movement of synaptic transmitter between different clusters is modeled as a Brownianlike stochastic process in which the transmitter distribution is adaptive at different firing phases. We experimented with a wide range of existing unsupervised SNN architectures and achieved consistent performance improvements. The improvements in classification accuracy have reached 1.99% and 6.29% on the MNIST and EMNIST datasets respectively.

SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs


Medical image segmentation is a critical field in the
domain of computer vision and with the growing acclaim of deep
learning based models, research in this field is constantly expanding.
Optical coherence tomography (OCT) is a non-invasive
method that scans the human’s retina with depth. It has been
hypothesized that the thickness of the retinal layers extracted
from OCTs could be an efficient and effective biomarker for
early diagnosis of AD. In this work, we aim to design a selftraining
model architecture for the task of segmenting the retinal
layers in OCT scans. Neural architecture search (NAS) is a
subfield of AutoML domain, which has a significant impact on
improving the accuracy of machine vision tasks. We integrate
the NAS algorithm with a Unet auto-encoder architecture as
its backbone. Then, we employ our proposed model to segment
the retinal nerve fiber layer in our preprocessed OCT images
with the aim of AD diagnosis. In this work, we trained a
super-resolution generative adversarial network on the raw OCT
scans to improve the quality of the images before the modeling
stage. In our architecture search strategy, different primitive
operations suggested to find down- & up-sampling Unet cell
blocks and the binary gate method has been applied to make
the search strategy more practical. Our architecture search
method is empirically evaluated by training on the Unet and
NAS-Unet from scratch. Specifically, the proposed NAS-Unet
training significantly outperforms the baseline human-designed
architecture by achieving 95.1% in the mean Intersection over
Union metric and 79.1% in the Dice similarity coefficient.

OCT Image Segmentation Using Neural Architecture Search and SRGAN

Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations.

Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning


For Tuberculosis (TB) detection beside the more expensive diagnosis solutions such as culture or sputum smear analysis one could consider the automatic analysis of the chest X-ray (CXR). This could mimic the lung region reading by the radiologist and it could provide an inexpensive solution to analyze and diagnose pulmonary abnormalities such as TB, a disease which often co-occurs with HIV. This software based pulmonary screening can be a reliable and affordable alternative solution for rural populations in different parts of the world such as the Indian subcontinent, Africa, etc.
The fully automatic system we are proposing is processing the incoming CXR image by applying image processing techniques to detect the region of interest (ROI) followed by a computationally cheap feature extraction involving edge detection using Laplacian of Gaussian which we enrich by counting the local distribution of the intensities. The choice to "zoom in" the ROI and look for abnormalities locally is motivated by the fact that some pulmonary abnormalities are localized in specific regions of the lungs. Later on the classifiers can decide about the normal or abnormal nature of each lung X-ray. Our goal is to find a simple feature descriptor, instead of a combination of several ones, -proposed and promoted in recent years' literature, which can properly and simply describe the different pathological alterations in the lungs. Our experiments report results on two publicly available benchmark data collections, namely the Shenzhen and the Montgomery collection.
For performance evaluation, measures such as area under the curve (AUC), and accuracy (ACC) were considered, achieving AUC = 0.81 (ACC = 83.33%) and AUC = 0.96 (ACC = 96.35%) for the Montgomery and Shenzhen collections, respectively. Several comparisons are also provided to other state-of-the-art systems reported recently in the field

Automatic Tuberculosis Detection Using Chest X-ray Analysis With Position Enhanced Structural Information


Darkness brings us uncertainty, worry and low confidence. This is a problem not only applicable to us walking in a dark evening but also for drivers driving a car on the road with very dim or even without lighting condition. To address this problem, we propose a new CNN structure named as Video Lightening Network (VLN) that regards the low-light enhancement as a residual learning task, which is useful as reference to indirectly lightening the environment, or for vision-based application systems, such as driving assistant systems. The VLN consists of several Lightening Back-Projection (LBP) and Temporal Aggregation (TA) blocks. Each LBP block enhances the low-light frame by domain transfer learning that iteratively maps the frame between the low- and normal-light domains. A TA block handles the motion among neighboring frames by investigating the spatial and temporal relationships. Several TAs work in a multi-scale way, which compensates the motions at different levels. The proposed architecture has a consistent enhancement for different levels of illuminations, which significantly increases the visual quality even in the extremely dark environment. Extensive experimental results show that the proposed approach outperforms other methods under both objective and subjective metrics.

Video Lightening with Dedicated CNN Architecture

We present a novel approach to developing an end-to-end learning framework to segment and classify major regions of a scientific document. We consider scientific document layout analysis as an object detection task over digital images, without any additional text features that need to be added into the network during the training process. Our technical objective is to implement transfer learning via fine-tuning of pre-trained networks and thereby demonstrate that this deep learning architecture is suitable for tasks that lack very large document corpora for training ab initio. As part of the experimental test bed for empirical evaluation of this approach, we created a merged multi-corpus data set for scientific publication layout detection tasks. Our results show good improvement with fine-tuning of a pre-trained base network using this merged data set, compared to the baseline convolutional neural network architecture.

Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks

Recently, various material recognition methods have
been introduced that uses a single color or light field camera. In
prior methods, color and texture information of an object are
used as key features. However, there exists fundamental limitation
in using color features for material recognition in that material
type can be characterized better by surface reflectance, visual
appearance rather than its color and textures. In this work, we
propose IR surface reflectance based material type recognition
method. We use off-the-shelf ToF camera to estimate the IR
reflectance of arbitrary surface. Material type recognition is
performed on both color and surface IR reflectance features. Several
network structures including gradual convolutional neural
network are proposed and verified for our material recognition
within our own 3D data sets.

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Mutual Information based Method for Unsupervised Disentanglement of Video Representation

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