Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-end Multi-task Learning of Missing Value Imputation and Forecasting in Time-Series Data

Multivariate time-series prediction is a common task, but it often becomes challenging due to missing data caused by unreliable sensors and other issues. In fact, inaccurate imputation of missing values can degrade the downstream prediction performance, so it may be better not to rely on the estimated values of missing data. Furthermore, observed data may contain noise, so denoising them can be helpful for the main task at hand. In response, we propose a novel approach that can automatically utilize the optimal combination of the observed and the estimated values to generate not only complete, but also noise-reduced data by our own gating mechanism. We evaluate our model on incomplete real-world time-series datasets and achieved state-of-the-art performance. Moreover, we present in-depth studies using a carefully designed, synthetic multivariate time-series dataset to verify the effectiveness of the proposed model. The ablation studies and the experimental analysis of the proposed gating mechanism show that it works as an effective denoising and imputation method for time-series classification tasks.
Mood detection analyzing lyrics and audio signal based on deep learning architectures

Digital era has changed the way music is produced and propagated creating new needs for automated and more effective management of music tracks in big volumes. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval) and connected with many research papers in the past few years. In order to approach the task of mood detection, we faced separately the analysis of musical lyrics and the analysis of musical audio signals. Then we applied a uniform multichannel analysis to classify our data in mood classes. The available data we will use to train and evaluate our models consists of a total of 2.000 song titles, classified in four mood classes {happy, angry, sad, relaxed}. The result of this process leads to a uniform prediction for emotional arousal that a music track can cause to a listener and show the way to develop many applications.
TAAN: Task-Aware Attention Network for Few-shot Classification

Few-shot classification aims to recognize unlabeled samples from unseen classes given only a few labeled samples. Current approaches of few-shot learning usually employ a metric-learning framework to learn a feature similarity comparison between a query (test) example and the few support (training) examples. However, these approaches all extract features from samples independently without looking at the entire task as a whole, and so fail to provide an enough discrimination to features. Moreover, the existing approaches lack the ability to select the most relevant features for the task at hand. In this work, we propose a novel algorithm called Task-Aware Attention Network (TAAN) to address the above problems in few-shot classification. By inserting a Task-Relevant Channel Attention Module into metric-based few-shot learners, TAAN generates channel attentions for each sample by aggregating the context of the entire support set and identifies the most relevant features for similarity comparison. The experiment demonstrates that TAAN is competitive in overall performance comparing to the recent state-of-the-art systems and improves the performance considerably over baseline systems on both mini-ImageNet and tiered - ImageNet benchmarks.
Multi-layered Discriminative Restricted Boltzmann Machine with Untrained Probabilistic Layer

An extreme learning machine (ELM) is a three-layered feed-forward neural network having untrained parameters, which are randomly determined before training. Inspired by the idea of ELM, a probabilistic untrained layer called a probabilistic-ELM (PELM) layer is proposed, and it is combined with a discriminative restricted Boltzmann machine (DRBM), which is a probabilistic three-layered neural network for solving classification problems. The proposed model is obtained by stacking DRBM on the PELM layer. The resultant model (i.e., multi-layered DRBM (MDRBM)) forms a probabilistic four-layered neural network. In MDRBM, the parameters in the PELM layer can be determined using Gaussian-Bernoulli restricted Boltzmann machine. Owing to the PELM layer, MDRBM obtains a strong immunity against noise in inputs, which is one of the most important advantages of MDRBM. Numerical experiments using some benchmark datasets, MNIST, Fashion-MNIST, Urban Land Cover, and CIFAR-10, demonstrate that MDRBM is superior to other existing models, particularly, in terms of the noise-robustness property (or, in other words, the generalization property).
Performance of Relay Selection for NOMA Based Cooperative Networks Over Shadowed Fading Channels

In this work, a non-orthogonal multiple access (NOMA) based transmission between a source and two end users is examined over slow Generalized-$K$ distributed (GKD) fading channels, where a single relay using decode-and-forward (DF) protocol is selected out of a set of full-duplex/half-duplex (FD/HD) multiple relays with the quality of service requirements. Two stage relay selection (RS), selecting a relay out of a set of relays providing service quality for user 1 to maximize data rate of user 2, is analyzed. Additionally, not only perfect successive interference cancellation (pSIC) but also imperfect SIC (ipSIC) is considered. Self-interference (SI) and ipSIC are assumed to be Nakagami-$m$ distributed, additionally, Gaussian and channel gain dependent modelling of ipSIC are also considered. A new expression for cumulative distribution function (CDF) of GKD is provided, thereafter, robust approximated probability density function (PDF) and CDF for GKD based on generalized Gauss-Laguerre quadrature are derived. Since outage probability (OP) analysis with exact CDF is found to be unstable, approximated and asymptotic OP expressions are derived and validated via Monte Carlo simulation technique. Approximated CDF, in turn OP, expression is almost excellent. Unlike existing works, our expressions are unique and valid for all cases such as FD and HD together with pSIC and ipSIC, i.e. expressions are not given separately but in a single compact form. Effects of SI, ipSIC, fading and shadowing parameters on OP are demonstrated and it is also shown that RS can significantly reduce OP. Finally, nonzero diversity order is asserted and details are provided.
An Iterative Hybrid JMCFIE-MSIE-PO Method for Analyzing EM Scattering From Complex Target Above Rough Surface

An iterative hybrid method is proposed for analyzing the electromagnetic (EM) scattering from complex target above rough surface. First, the electric/magnetic current-combined field integral equation (JMCFIE) is formulated to calculate the EM scattering from the complex target combining conductor and multiple dielectric objects. Then, the single integral equation (SIE) method for multiple dielectric objects in the complex target (MSIE) is applied to reduce one-half of the unknowns in dielectric regions, and the hybrid JMCFIE-MSIE method is formulated. Afterward, physical optics (PO) approximation is employed for computing the EM scattering on the rough surface, and the iterative hybrid based on the combination of JMCFIE-MSIE and PO is derived for composite scattering from complex target above rough surface. Furthermore, the multilevel fast multipole algorithm is utilized to accelerate the matrix-vector multiplications in the computation process. The equivalent edge currents method is applied to meliorate the result. Finally, the validation and applicability of the proposed iterative solution are demonstrated by several numerical examples.
Compact and discriminative multi-object tracking with siamese CNNs

Following the tracking-by-detection paradigm, multiple object tracking deals with challenging scenarios, occlusions or even missing detections; the priority is often given to quality measures instead of speed, and a good trade-off between the two is hard to achieve. Based on recent work, we propose a fast, lightweight tracker able to predict targets position and reidentify them at once, when it is usually done with two sequential steps. To do so, we combine a bounding box regressor with a target-oriented appearance learner in a newly designed and unified architecture. This way, our tracker can infer the targets' image pose but also provide us with a confidence level about target identity. Most of the time, it is also common to filter out the detector outputs with a preprocessing step, throwing away precious information about what has been seen in the image. We propose a tracks management strategy able to balance efficiently between detection and tracking outputs and their associated likelihoods. Simply put, we spotlight a full siamese based single object tracker able to predict both position and appearance features at once with a light-weight and all-in-one architecture, within a balanced overall multi-target management strategy. We demonstrate the efficiency and speed of our system w.r. t the literature on the well-known MOT17 challenge benchmark, and bring to the fore qualitative evaluations as well as state-of-the-art quantitative results.
Mutual Alignment between Audiovisual Features for End-to-End Audiovisual Speech Recognition

Asynchronization issue caused by different types of modalities is one of the major problems in audio visual speech recognition (AVSR) research. However, most AVSR systems merely rely on up sampling of video or down sampling of audio to align audio and visual features, assuming that the feature sequences are aligned frame-by-frame. These pre-processing steps oversimplify the asynchrony relation between acoustic signal and lip motion, lacking flexibility and impairing the performance of the system. Although there are systems modeling the asynchrony between the modalities, sometimes they fail to align speech and video precisely over some even all noisy conditions. In this paper, we propose a mutual feature alignment method for AVSR which can make full use of cross modility information to address the asynchronization issue by introducing Mutual Iterative Attention (MIA) mechanism. Our method can automatically learn an alignment in a mutual way by performing mutual attention iteratively between the audio and visual features, relying on the modified encoder structure of Transformer. Experimental results show that our proposed method obtains absolute improvements up to 20.42% over the audio modality alone depending upon the signal-to-noise-ratio (SNR) level. Better recognition performance can also be achieved comparing with the traditional feature concatenation method under both clean and noisy conditions. It is expectable that our proposed mutual feature alignment method can be easily generalized to other multimodal tasks with semantically correlated information.
Motion Complementary Network for Efficient Action Recognition

Both two-stream ConvNet and 3D ConvNet are widely used in action recognition. However, both methods are not efficient for deployment: calculating optical flow is very slow, while 3D convolution is computationally expensive. Our key insight is that the motion information from optical flow maps is complementary to the motion information from 3D ConvNet. Instead of simply combining these two methods, we propose two novel techniques to enhance the performance with less computational cost: fixed-motion-accumulation and balanced-motion-policy. With these two techniques, we propose a novel framework called Efficient Motion Complementary Network(EMC-Net) that enjoys both high efficiency and high performance. We conduct extensive experiments on Kinetics, UCF101, and Jester datasets. We achieve notably higher performance while consuming 4.7× less computation than I3D, 11.6× less computation than ECO, 17.8× less computation than $\mathrm{R}(2+1)\mathrm{D}$. On Kinetics dataset, we achieve 2.6% better performance than the recent proposed TSM with 1.4× fewer FLOPs and 10ms faster on K80 GPU.
Surface IR Reflectance Estimation and Material Recognition using ToF Camera

Recently, various material recognition methods have been introduced that use 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 datasets.
Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review

A health technology assessment (HTA) is commonly defined as a multidisciplinary approach used to evaluate medical, social, economic, and ethical issues related to the use of a health technology in a systematic, transparent, unbiased, robust manner. To help inform HTA recommendations, the surveillance of social media platforms can provide important insights to the clinical community and to decision makers on the effectiveness and safety of the use of health technologies on a patient. A scoping review of the published literature was performed to gain some insight on the accuracy and automation of sentiment analysis (SA) used to assess public opinion on the use of health technologies. A literature search of major databases was conducted. The main search concepts were SA, social media, and patient perspective. Among the 1,776 unique citations identified, 12 studies that described the use of SA methods to evaluate public opinion on or experiences with the use of health technologies as posted on social media platforms were included. The SA methods used were either lexicon- or machine learning-based. Two studies focused on medical devices, three examined HPV vaccination, and the remaining studies targeted drug therapies. Due to the limitations and inherent differences among SA tools, the outcomes of these applications should be considered exploratory. The results of our study can initiate discussions on how the automation of algorithms to interpret public opinion of health technologies should be further developed to optimize the use of data available on social media.
Facial Expression Recognition Using Residual Masking Network

Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking Idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets.
Using Scene Graphs for Detecting Visual Relationships

In this paper we solve the problem of detecting relationships between pairs of objects in an image. We develop spatially aware word embeddings using scene graphs and use joint feature representations containing visual, spatial and semantic embed-dings from the input images to train a deep network on the task of relationship detection. Further, we propose to utilize context aligned scene graph embeddings from the train set, without requiring explicit availability of scene graphs at test time. We show that the proposed method outperforms the state-of-the-art methods for predicate detection and provides competing results on relationship detection. We also show the generalization ability of the proposed method by performing predictions under zero shot settings. Further, we also provide an exhaustive empirical evaluation on each component of the proposed network.
A Linear Eddy Current Speed Sensor With a Perpendicular Coils Configuration

This paper presents a new linear eddy current speed sensor with rectangular-shaped coils. The excitation coil and the pick-up coil have a perpendicular configuration without a magnetic yoke. The proposed sensor is shorter than the previous designs. The sensor works for a conductive moving target; in this paper we present calculations and an experimental verification for solid iron and aluminum moving part materials. A novel 3D analytical method is presented for the description and for the design of an eddy current speed sensor that is fast and has high precision. The source fields and the reaction fields caused by induced eddy currents are separated in our 3D analytical method, which facilitates an enhanced investigation of the features of the speed sensor. Evaluations of the effects of the moving part material and of coil lift off on the performance of the speed sensor are made with the use of a 3D analytical method. Measurements are performed for an eddy current speed sensor at different speeds up to 11.65 m/s and at different frequencies with a novel analytical model in terms of the induced voltage in the pickup coil versus speed. Simplicity and high precision are the main advantages of the proposed speed sensor. The achieved linearity error is 0.47% (measured) up to 11.6 m/s, and 0.43% (calculated) up to 117 m/s (420 km/h).
Inception-based Deep Learning Architecture for Tuberculosis Screening using Chest X-rays

The motivation for this work is the primary need of screening Tuberculosis (TB) positive patients in the severely resource constrained regions of the world. Chest X-ray (CXR) is considered to be a promising indicator for the onset of TB, but the lack of skilled radiologists in such resource constrained regions degrades the situation. Therefore, several computer aided diagnosis (CAD) systems have been proposed to solve the decision making problem, which includes hand-engineered feature extraction methods to deep learning or Convolutional Neural Network (CNN) based methods. Feature extraction, being a time and resource intensive process, often delays the process of mass screening. Hence, an end to end CNN architecture is proposed in this work to solve the problem. Two benchmark CXR datasets have been used in this work, collected from Shenzhen (China) and Montgomery County (USA), on which the proposed methodology achieved a maximum abnormality detection accuracy (ACC) of 91.7% (0.96 AUC) and 87.47% (0.92 AUC) respectively. Considering these datasets, to the best of our knowledge, the obtained results are superior to the state of the art deep learning based works.
DE-Net: Dilated Encoder Network for Automated Tongue Segmentation

Automated tongue recognition is a growing research field due to global demand for personal health care. Using mobile devices to take tongue pictures is convenient and of low cost for tongue recognition. It is particularly suitable for self-health evaluation of the public. However, images taken by mobile devices are easily affected by various imaging environment, which makes fine segmentation a more challenging task compared with those taken by specialized acquisition devices. Deep learning approaches are promising for tongue image segmentation because they have powerful feature learning and representation capability. However, the successive pooling operations in these methods lead to loss of information on image details, making them fail when segmenting low-quality images captured by mobile devices. To address this issue, we propose a dilated encoder network (DE-Net) to capture more high-level features and get high-resolution output for automated tongue image segmentation. In addition, we construct two tongue image datasets which contain images taken by specialized devices and mobile devices, respectively, to verify the effectiveness of the proposed method. Experimental results on both datasets demonstrate that the proposed method outperforms the state-of-the-art methods in tongue image segmentation.
Ultrasound Image Restoration Using Weighted Nuclear Norm Minimization

Ultrasound images are often contaminated by speckle noise during the acquisition process, which influences the performance of subsequent applications. The paper introduces a nonconvex low-rank matrix approximation model for ultrasound images restoration, which integrates the weighted nuclear norm minimization (WNNM) and data fidelity term. WNNM can adaptively assign weights on different singular values to preserve more details in restored images. The fidelity term about ultrasound images do not be utilized in existing low-rank ultrasound denoising methods. This optimization question can effectively solved by alternating direction method of multipliers (ADMM). The experimental results on simulated images and real medical ultrasound images demonstrate the excellent performance of the proposed method compared with other four state-of-the-art methods.
Vector Modulator-Based Analog Beamforming Using the Least Euclidean Distance Criterion

In traditional multiple-input multiple-output (MIMO) receivers, radio frequency (RF) front-ends are exposed to interference as no analog spatial filtering is employed before the digital beamforming stage. Therefore the RF front-end is power-hungry, and analog to digital converters require a high dynamic range. In this paper, we consider an analog beamforming system in case of narrowband signals to cancel interference early in the analog domain, thus reducing the required ADC resolution. In contrast to existing analog beamformers with only phase shifts, our proposed design employs vector modulators where the coefficients can be selected from a set of weights with variable phases and amplitudes. We also propose an efficient and fast Euclidean distance algorithm to determine the analog beamformer coefficients while being suitable for realistic scenarios. Finally, an expression is introduced to estimate the interference rejection achieved by employing the proposed algorithm and a vector modulator in the RF domain. The introduced algorithm leads to considerable improvement in computational complexity by slightly sacrificing interference rejection.
Operation and Topology Aware Fast Differentiable Architecture Search

Differentiable architecture search (DARTS) has gained significant attention amongst neural architecture search approaches due to its effectiveness in finding competitive network architectures with affordable computational complexity. However, DARTS’ search space is designed such that even a randomly sampled architecture performs reasonably well. Moreover, due to the complexity of search architectural building block or cell, it is unclear whether these are certain operations or the cell topology that contributes most to achieving higher final accuracy. In this work, we dissect the DARTS's search space to understand which components are most effective in producing better architectures. Our experiments show that: (1) Good architectures can be discovered regardless of the search network depth; (2) Seperable convolution with 3x3 kernel is the most effective operation in this search space; and (3) The cell topology also has substantial effect on the accuracy. Based on these insights, we propose an efficient search approach referred to as eDARTS, which searches on a pre-specified cell having good topology with increased attention to important operations, using a shallow search supernet. Moreover, we propose some optimizations for eDARTS that significantly speed up the search as well as alleviate the well known skip connection aggregation problem of DARTS. eDARTS achieves an error rate of 2.53% on CIFAR-10 using a 3.1M parameters model whereas the search cost is less than 30 minutes.