Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise Temporal Localization for Complete Actions with Quantified Temporal Structure

Existing temporal action detection algorithms cannot distinguish complete and incomplete actions while this property is essential in many applications. To tackle this challenge, we proposed the action progression networks (APN), a novel model that predicts action progression of video frames with continuous numbers. Using the progression sequence of test video, on the top of the APN, a complete action searching algorithm (CAS) was designed to detect complete actions only. With the usage of frame-level fine-grained temporal structure modeling and detecting actions according to their whole temporal context, our framework can locate actions precisely and is good at avoiding incomplete action detection. We evaluated our framework on a new dataset (DFMAD-70) collected by ourselves which contains both complete and incomplete actions. Our framework got good temporal localization results with 95.77% average precision when the IoU threshold is 0.5. On the benchmark THUMOS14, an incomplete-ignostic dataset, our framework still obtain competitive performance. The code is available online at https://github.com/MakeCent/Action-Progression-Network
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification

In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples. In this context, works have proposed to introduce Graph Neural Networks (GNNs) aiming at exploiting the information contained in other samples treated concurrently, what is commonly referred to as the transductive setting in the literature. These GNNs are trained all together with a backbone feature extractor. In this paper, we propose a new method that relies on graphs only to interpolate feature vectors instead, resulting in a transductive learning setting with no additional parameters to train. Our proposed method thus exploits two levels of information: a) transfer features obtained on generic datasets, b) transductive information obtained from other samples to be classified. Using standard few-shot vision classification datasets, we demonstrate its ability to bring significant gains compared to other works.
A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification

Malware classification helps to understand its purpose and is also an important part of attack detection. And it is also an important part of discovering attacks. Due to continuous innovation and development of artificial intelligence, it is a trend to combine deep learning with malware classification. In this paper, we propose an improved malware image rescaling algorithm (IMIR) based on local mean algorithm. Its main goal of IMIR is to reduce the loss of information from samples during the process of converting binary files to image files. Therefore, we construct a neural network structure based on VGG model, which is suitable for image classification. In the real world, a mass of malware family labels are inaccurate or lacking. To deal with this situation, we propose a novel method to train the deep neural network by Semi-supervised Generative Adversarial Network (SGAN), which only needs a small amount of malware that have accurate labels about families. By integrating SGAN with weak coupling, we can retain the weak links of supervised part and unsupervised part of SGAN. It improves the accuracy of malware classification by making classifiers more independent of discriminators. The results of experimental demonstrate that our model achieves exhibiting favorable performance. The recalls of each family in our data set are all higher than 93.75%.
Global NO2 Dynamics During the COVID-19 Pandemic: A Comparison Between Two Waves of the Coronavirus

Nitrogen dioxide (NO2) as one of the key pollutants in the atmosphere, is an important trace gas of traffic emissions or industrial activities. The coronavirus disease (COVID-19) epidemic preventions have significantly reduced the emissions of NO2, which provided an opportunity to disentangling the contributions to global emissions in major epidemic regions. Here, we quantified the influences of lockdown measures on the NO2 concentration at a global scale during the two waves of the COVID-19 epidemic, and disentangled the relative contributions of the major epidemic regions (i.e., China, Europe, and USA) on the global NO2 densities due to the lockdown measures. Our results revealed that there was an evident decrease of global NO2 in 2020, especially in China, Europe, and USA. The global mean NO2 densities decreased approximately 2.1 and 3.9 μmol/m2 in the first and second waves of the epidemic. While in the Northern Hemisphere, NO2 densities decreased 6.6 μmol/m2 on average during the first stage of the epidemic, and a slight increase (i.e., 1.7 μmol/m2) in the second wave of the epidemic. The magnitudes and durations of the second wave were much smaller than the first wave of the coronavirus infections. The strict lockdown measures implemented in China significantly decreased the NO2 concentration, which therefore the largest contributor in the first wave decreases of global average NO2 densities. Intervention at an early stage would be beneficial to the preventions of epidemic situation. Our study could provide references for economic loss assessments and economic recovery strategies during the stages ofpost-pandemic.
A Deep Motion Deblurring Network Using Channel Adaptive Residual Module

In this paper, we solve the problem of dynamic scenes deblurring with motion blur. Restoration of images in the presence of motion blur necessitates a network design that the receptive field can completely cover all areas that need to be deblurred, while the existing network increases the receptive field by continuously stacking the ordinary convolutional layer or increasing the size of the convolution kernel. However, these methods inevitably increase the computational burden of the network. We propose a novel architecture consisting of a channel adaptive residual module. Different features of the blurred image are extracted and distributed on each feature channel. Our network can calculate the weight of each channel through learning, and extract the image features adaptively according to different degrees of blurring and importance of information. We embed the module in a modified encoder-decoder design with skip connections to achieve multi-scale feature fusion for further performance improvement. The extensive comparison with the existing techniques in the baseline dynamic scene deblurring dataset shows that the proposed network can effectively realize image deblurring, and the accuracy and speed are comparable with the existing techniques.
Reducing False Positives in Object Tracking with Siamese Network

We propose a robust long-term object tracking method that resolves the fundamental cause of the drift and loss of a target in visual object tracking. The proposed method consists of “sampling area extension”, which prevents a tracking result from drifting to other objects by learning false positive samples in advance (before they enter the search region of the target), and “adaptive search based on motion models”, which prevents a tracking result from drifting to other objects and avoids the loss of the target by using not only appearance features but also motion models to adaptively search for the target. Experiments conducted on long-term tracking dataset showed that our first technique improved robustness by 16.6% while the second technique improved robustness by 15.3%. By combining both, our method achieved 21.7% and 9.1% improvement for the robustness and precision, and the processing speed became 3.3 times faster. Additional experiments showed that our method achieved the top robustness among state-of-the-art methods on three long-term tracking datasets. These findings demonstrate that our method is effective for long-term object tracking and that its performance and speed are promising for use in practical applications of various technologies underlyina object tracking,
A Lightweight Network to Learn Optical Flow from Event Data

1* 1contributed equally and should be regarded as co-first authors. Existing deep neural networks have found success in estimation of event-based optical flow, but are at the expense of complicated architectures. Moreover, few prior works discuss how to tackle with the noise problem of event camera, which would severely contaminate the data quality and make estimation an ill-posed problem. In this work, we present a lightweight pyramid network with attention mechanism to learn optical flow from events data. Specially, the network is designed according to two-well established principles: Laplacian pyramidal decomposition and channel attention mechanism. By integrating Laplacian pyramidal processing into CNN, the learning problem is simplified into several subproblems at each pyramid level, which can be handled by a relatively shallow network with few parameters. The channel attention block, embedded in each pyramid level, treats channels of feature map unequally and provides extra flexibility in suppressing background noises. The size of the proposed network is about only 5% of previous methods while our method still achieves state-of-the-art performance on the benchmark dataset. The experimental video samples of continuous flow estimation is presented at: https://github.com/xfleezy/blob.
CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in the extracted building maps. Second, the similarity between buildings and background results in intra-class inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block (DBB). Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then, to handle intra-class inconsistency, we construct Spatial Channel Attention Block (SCAB). It combines context space information and selects more distinguishable features from space and channel. Finally, we propose a novel loss function to enhance the purpose of loss by adding evaluation indicator. Based on our proposed CT-UNet, we achieve 85.33% mean IoU on the Inria dataset and 91.00% mean IoU on the WHU dataset, which outperforms our baseline (U-Net ResNet-34) by 3.76% and Web-Net by 2.24%.
A Plane-based Approach for Indoor Point Clouds Registration

Iterative Closest Point (ICP) is one of the mostly used algorithms for 3D point clouds registration. This classical approach can be impacted by the large number of points contained in a point cloud. Planar structures, which are less numerous than points, can be used in well-structured man-made environment. In this paper we propose a registration method inspired by the ICP algorithm in a plane-based registration approach for indoor environments. This method is based solely on data acquired with a LiDAR sensor. A new metric based on plane characteristics is introduced to find the best plane correspondences. The optimal transformation is estimated through a two-step minimization approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) dataset show that the proposed method enables to successfully register 100 % of the scans from the three indoor sequences. Experiments also show that the proposed method is more robust in large motion scenarios than other state-of-the-art algorithms.
Direction-of-Arrival Estimation With Circular Array Using Compressed Sensing in 20 GHz Band

The super-high-frequency band is used in the fifth-generation (5G) mobile communication system, and the introduction of massive multiple-input–multiple-output (MIMO) to 5G is being considered. In an environment with substantial propagation loss, such as that in the case of millimeter waves, massive MIMO can realize beamforming according to the position of terminal stations. Therefore, it is important to understand the direction of arrival of millimeter waves. In this letter, we clarify the relationship between power, delay time, and direction of arrival while focusing on the horizontal direction, using a 48-element massive array in the 20 GHz band in urban Japan. In addition, we obtain direction-of-arrival (DoA) estimation results using an iterative reduction threshold algorithm (ISTA), which is based on compressed sensing. It is shown that the dominant path can be estimated by using the ISTA even with approximately five elements.
Automatic Annotation of Corpora For Emotion Recognition Through Facial Expressions Analysis

The massive adoption of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions on a large variety of topics. Research has made many efforts in defining accurate algorithms for the analysis of emotions conveyed by texts, however their performance often relies on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually -engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.
Low-Sidelobe Cavity-Backed Slot Antenna Array With Simplified Feeding Structure for Vehicular Communications

In this article, low-sidelobe cavity-backed slot antenna array with simplified feeding structure for vehicular communication is proposed. Conventional low-sidelobe antennas were realized using power dividing network to obtain the in-phase and nonuniform-amplitude excitation, which causes complicated antenna's structure and design. To tackle these challenges, a simplified feeding method without using power dividing network is introduced to design the low-sidelobe slot antenna array. The slots at the top walls serve as the radiation elements, and the slots are directly fed by the electric field of the cavity mode. Nonuniform amplitude is obtained by simply modifying the slots’ sizes and positions, while the in-phase excitation of the elements is remained. The nonuse of power dividers brings out a simpler antenna structure than conventional full-metal waveguide-based antennas. Besides, the full-metal structure introduces a high power-handling capacity. Then, two linear arrays with 1 × 4, 1 × 7 elements and a planar array with 5×4 elements are presented to show the design feasibility. Finally, the 5 × 4 antenna array is fabricated and measured, which can achieve 18.2 dBi gain, −20 dB sidelobe level, 94% radiation efficiency, and -42 dB cross-polarization. Good agreement between measurement and simulation verifies the feasibility of the proposed design concept.
Writer Identification Using Deep Neural Networks: Impact of Patch Size and Number of Patches

Traditional approaches for the recognition or identification of the writer of a handwritten text image used to relay on heuristic knowledge about the shape and other features of the strokes of previously segmented characters. However, recent works have done significantly advances on the state of the art thanks to the use of various types of deep neural networks. In most of all of these works, text images are decomposed into patches, which are processed by the networks without any previous character or word segmentation. In this paper, we study how the way images are decomposed into patches impact recognition accuracy, using three publicly available datasets. The study also includes a simpler architecture where no patches are used at all – a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis. Results show that bigger patches generally lead to improved accuracy, achieving in one of the datasets a significant improvement over the best results reported so far.
Investigation of DNN Model Robustness Using Heterogeneous Datasets

Deep learning frameworks have been successfully applied to tackle many challenging tasks in pattern recognition and computer vision thanks to its ability to automatically extract representative features from the training data. Such type of data-driven approach, however, is subject to the criticism of too much dependency on the training set. In this research, we attempt to investigate the validity of this statement: ‘deep learning is only as good as its data’ by evaluating the performance of deep learning models using heterogeneous data sets, in which distinct representations of the same source data are employed for training/testing. We have examined three cases: low-resolution image, severely compressed input and halftone image in this work. Our preliminary results indicate that such dependency indeed exists. Classifier performance drops considerably when the model is tested with modified or transformed input. The best outcomes are obtained when the model is trained with hybrid input.
Graph-based image decoding for multiplexed in situ RNA detection

Image-based multiplexed in situ RNA detection makes it possible to map the spatial gene expression of hundreds to thousands of genes in parallel, and thus discern at the same time a large numbers of different cell types to better understand tissue development, heterogeneity, and disease. Fluorescent signals are detected over multiple fluorescent channels and imaging rounds and decoded in order to identify RNA molecules in their morphological context. Here we present a graph-based decoding approach that models the decoding process as a network flow problem jointly optimizing observation likelihoods and distances of signal detections, thus achieving robustness with respect to noise and spatial jitter of the fluorescent signals. We evaluated our method on synthetic data generated at different experimental conditions, and on real data of in situ RNA sequencing, comparing results with respect to alternative and gold standard image decoding pipelines.
IoT-Enabled Proposal for Adaptive Self-Powered Renewable Energy Management in Home Systems

The new generation of communication networks can provide massive connectivity of devices, extremely low latency, higher capacity, and increased bandwidth. These features enable the deployment of management systems in different sectors such as energy and in a wide variety of environments such as agriculture, surveillance or home systems. In this regard, this paper proposes a self-powered adaptive and automated home energy control system enabled by the Internet of Things (IoT) technologies. The system aims to adapt the consumption patterns to the availability of self-generated renewable energy (produced from solar panels, wind-mills, etc.). Therefore, the renewable and non-renewable supply from the power grid is considered a secondary power source. As part of the proposal, this paper presents the consumption negotiation scheme for IoT devices, the management mechanisms to optimize the use of the available energy, and the related model. Given the complexity of the adaptive management process, the proposal also presents a heuristic strategy based on a prepartitioning method to obtain feasible solutions in a reasonable running time. The simulation results for offline and online scenarios validate the advantages of the proposed strategy, and the numerical improvements are presented.
Adaptive Word Embedding Module for Semantic Reasoning in Large-scale Detection

In recent years, convolutional neural networks have achieved rapid development in the field of object detection. However, due to the imbalance of data, high costs in labor and uneven level of data labeling, the overall performance of the previous detection network has dropped sharply when dataset extended to the large-scale with hundreds and thousands of categories. The embedding of external semantic knowledge can bring new additional information to reduce the cost of annotation and improve the performance of large-scale detection. In this paper, the Adaptive Word Embedding Module is proposed to extract the adaptive semantic knowledge graph under the constraint of the semantic consistency. Our method endows the ability to infer global semantic of detection networks without other attribute or relationship annotations. Compared with Faster RCNN, the algorithm on the MSCOCO dataset was significantly improved by 4.1%, and the mAP value has reached 32.8%. On the VG1000 dataset, it increased by 0.9% to 6.7% compared with Faster RCNN. Adaptive Word Embedding Module is lightweight, general-purpose and can be plugged into diverse detection networks. Code will be made available.
A Family of High Step–Up DC–DC Converters With Nc Step-Up Cells and M–Source Clamped Circuits

In this paper, in order to study the relationship among different converters, a novel concept—modular high step-up converter—is proposed, which is composed of one basic converter, one Nc step-up cell (Nc-SUC), and one M-source passive lossless clamped circuit (PLCC). Nc means the number of capacitors in step-up cell and M-source represents the number of capacitors in PLCC. Nc-SUCs composed of many voltage lift cells (VLCs) and many constant voltage lift cells (CVLCs) are proposed and their general voltage gain formula is deduced. Each type of Nc-SUCs has Nc+1 different structures and the same voltage gain formula. Different Nc-SUCs can converse each other by adding or removing VLC and CVLC. Then, the general voltage gain formula of the proposed converters with Nc-SUCs is deduced, which is an additive combination of the voltage gain of Nc-SUCs and basic converter. Besides, a novel clamp circuit called M-source PLCC which is consisted of M+1 capacitors and one diode is presented. M capacitors of the M-source PLCC come from the Nc-SUC of converter, so only one capacitor and one diode are added. In comparison with classical PLCC, it makes the converters with Nc-SUCs not only possible to recycle the energy stored in the leakage inductor, but also further reduce the voltage stresses of the switch and the output capacitor, further improve the voltage gain, and even realize a three-level converter. Compared with the classical PLCC, the cost in M-source PLCC is unchanged. As M increases, the number and the variety of M-source PLCCs will also increase. Therefore, different M-source PLCCs can be chosen for the same converter to recycle the energy stored in the leakage inductor. If adopting converse thinking, based on the general voltage gain formula, removing basic converter and M-source PLCC, then, the number and the combination way of VLC and CVLC of different Nc-SUCs will expose the relationship among different high step-up converter to researcher. Finally, an improved converter based on 3-SUC as a representative of the deduced converters is proposed and analyzed in detail in the laboratory to verify performances.