Reviewerly/exHarmony huggingface dataset:
CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection

The computer vision community has made tremendous progress in solving a variety of semantic image understanding tasks, such as classification and segmentation.With the advancement of imaging technology and hardware, image semantic segmentation, through the use of deep learning, is among the most common topics which have been worked on in the last decade.However, image semantic segmentation suffers from several drawbacks such as insufficient detection of object boundaries.In this study, we present a new convolutional neural network architecture called CSU-Net that aims to self-enhance the results of semantic segmentation.The proposed model consists of two strongly concatenated encoder-decoder blocks.With this design, we reduced requirements on computing power and memory size to decrease costs and increase the training/prediction speed.This study also demonstrates the advantage of the proposed system for small training data sets.The proposed approach has been implemented on our private dataset, as well as on a publicly available dataset.A comparative analysis was carried out with four popular segmentation models and three other recently introduced architectures to show the efficiency of the proposed system.CSU-Net outperformed the other competing neural networks that we considered for the comparative study.As an example, it succeeded in improving the traditional U-Net result by approximately 50% in mean Intersection over Union (mIoU) for both tested datasets.Based on our experience, the CSU-Net can improve results of semantic segmentation in many applications.
Fast and Effective Scene Segmentation Method for Luminance Adjustment of Multi-Exposure Image

Multi-exposure image fusion is an important task for high dynamic range imaging. The performance of a fusion method is highly dependent on the quality of the input multi-exposure image. However, it is often difficult to obtain a multi-exposure image thoroughly covering the dynamic range of a scene. In such a situation, some areas are unclear in all the images contained by the multi-exposure image, leading to a low-visibility fusion result. To overcome this problem, Kinoshita and Kiya proposed scene segmentation-based luminance adjustment (SSLA). In SSLA, the scene is segmented into regions and the luminance of each region is adjusted so that the region has mid-level brightness. A fusion result with better visibility can be obtained by fusing the luminance adjusted multi-exposure image. Kinoshita and Kiya proposed two scene segmentation approaches for SSLA. One of them is fast but sometimes fails to segment dark areas and bright areas into different regions. The other can generate a better scene segmentation result but it requires a time-consuming iterative process. In this paper, we propose a new scene segmentation method for SSLA. The proposed scene segmentation method uses information obtained from the nonlinear luminance distribution and area occupancy of the segmented regions. A fast implementation for the proposed scene segmentation method is also described. Experimental results show that the proposed scene segmentation method is fast and stably generates good scene segmentation results.
Network Traffic Classification through Machine Learning methods in IoT Networks

IoT refers to a wide variety of embedded devices which are connected to the internet. These devices share data with each other by using wireless technology. The regular monitoring of IoT devices is mandatory for proper functioning of devices. Classification of network traffic is mandatory for QoS purpose. Implementation of QoS is mandatory for proper functioning of network. In this research TON_IOT dataset is used for network traffic classification purpose. Dataset contains all the features related to network traffic. Network traces data is firstly preprocessed to extract significant features. after feature selection machine learning algorithms are applied on these features. ML steps like standardization, normalization and one hot encoding is applied on the dataset. At the end, we will evaluate the performance of our model with other state of the art algorithms. KNN algorithm achieves highest accuracy with 99.9% classification rate.
BMI Estimation from 2D Face Images Using Support Vector Machine

Several studies on predicting Body Mass Index (BMI) based on face images have already been conducted. However, a lack of study employs machine learning to predict BMI. Furthermore, these studies only include Caucasian and African subjects, indicating that Asians are lacking. A system that utilizes the Support Vector Machine (SVM) algorithm was developed in this study to classify BMI from 2D face images by analyzing facial features. The Raspberry Pi 3 Model B+ was employed as the main microcomputer, and Raspberry Pi Camera version 1.3 was utilized to capture face images. The classifier model was trained with five hundred two (502) samples, including various ethnicities, age groups, and weight categories. They are underweight, normal, overweight, and obese. The system was tested on twenty-five (25) subjects for each of the four (4) BMI classes with a total of one hundred (100) samples. The results of the system were evaluated using a confusion matrix and obtained an accuracy score of 91%. This demonstrates that the SVM for BMI estimation has a high accuracy rate.
Sample-Efficient Reinforcement Learning for Pose Regulation of a Mobile Robot

Reinforcement Learning (RL) has gained interest in the control and automation communities thanks to its encouraging results in many challenging control problems without requiring a model of the system and of the environment. Yet, it is well-known that employing such a learning-based approach in real scenarios may be problematic, as a prohibitive amount of data might be required to converge to an optimal control policy. In this work, we equip a popular RL algorithm with two tools to improve exploration effectiveness and sample efficiency: the Episodic Noise, that helps useful subsets of actions emerge already in the first few training episodes, and the Difficulty Manager, that generates goals proportioned to the current agent’s capabilities. We demonstrate the effectiveness of such proposed tools on a pose regulation task of a four wheel steering four wheel driving robot, suitable for a wide range of industrial scenarios. The resulting agent learns effective sets of actions in just a few hundreds training epochs, reaching satisfactory performance during tests.
Computed Tomography Image of Lung: A Visualization Enhancement Approach Based on Unsharp Marking and Thresholded Local Intensity Area Descriptor

Viewing improvement is an important step in lung computed tomography (CT) image analysis. It assists to make diagnosis more accurate. In this paper, a new viewing improvement method for lung CT images is introduced. The proposed method consists of two stages. In the first stage, the image was deblurred and sharpened using the unsharp masking method. In the second stage, pre-processed image was enhanced the viewing by a modified method, thresholded local intensity area descriptor. The experiments were conducted on lung CT images from the LIDC-IDRI database. For the viewing improvement evaluation, our method was evaluated subjectively and quantitatively by five different measures of enhancement: Absolute mean brightness error, Edge content, Entropy, Peak signal-to-noise ratio, and Tenengrad criterion. The results clearly indicated that our approach was better than eight contrast enhancement methods. The proposed approach not only improved the contrast of the lung CT images, but also retained information essential for clinical diagnosis. Thus, it is expected to assist in improving the accuracy of diagnosis by physicians and computer-aided detection/diagnosis systems.
Multi-Sensor Control for Jointly Searching and Tracking Multi-Target Using the Poisson Multi-Bernoulli Mixture Filter

In this paper, a multi-sensor control method with limited sensor field-of-view (FoV) based on Poisson multi-Bernoulli mixture (PMBM) filter is proposed, which provides a solution to the multi-target joint search and tracking problem. First, we propose a robust fusion method for multi-sensor PMBM posterior densities with limited sensor FoV based on the generalized covariance intersection (GCI) fusion criterion. Then, a cost function is constructed for multi-sensor control based on the fused density, which takes into account both discovered and undiscovered targets. The optimal multi-sensor control scheme is obtained by minimizing the cost function. The effectiveness of the proposed control method is demonstrated through a multi-target joint search and tracking simulation with unknown and time-varying number of targets and limited FoVs of sensors.
Hands Tracking over Steering Wheel based on Multi-Bernoulli Filter Framework with Hand Landmarks Detector and Semantic Segmentation

Car driver’s hands motion operating the steering-wheel in a car cabin has been tracked based on multi-Bernoulli filter framework with the aid of semantic segmentation for body region extraction and hand landmarks detection over camera image, according to the newly proposed method in this paper. Real-time implementation of the method has been proposed as a hybrid system of python and C/C++ languages with CUDA GPU computation for particle filtering, DeepLab v3 for semantic segmentation, and MediaPipe for hand landmarks detector. Experimental results demonstrate improved performance of hands tracking especially by newly incorporated part of the hand landmarks detector.
Message passing based data association algorithm for multiple extended object Tracking

Due to the development of sensor technology in recent years, it has become increasingly common for objects to occupy multiple units of sensor resolution. For tracking an extended object (EO) that produces an unknown number of measurements per time, the main challenge is to identify the origin of the measurements. Rather than enumerating all measurements partitions or association hypotheses, we adopt a message passing inference method to reduce the computational complexity of obtaining the marginal association probabilities. An overcomplete description of data association uncertainty has been introduced to obtain the marginal association probabilities with linear complexity in the number of measurements and targets. Based on factor graph theory and the sum-product algorithm (SPA), the proposed algorithm has a satisfied performance compared to other data association methods.
Expanding Intra-class Difference and Boosting Frame-level Classification for Continuous Sign Language Recognition

Continuous Sign Language Recognition (CSLR) is a typical sequence-to-sequence (seq2seq) task from sign language video frame sequence to gloss sequence. Due to the annotation bottleneck in CSLR, there are usually weakly supervised annotations in existing sign language datasets. Therefore, it is difficult to train a deep learning model for CSLR. To solve the above difficulty, we propose the concept of expanding intra-class difference by expanding original labels and inter-class similarity filtering. And we fuse contextual features and spatial features to boost frame-level classification. We validated the effectiveness of the proposed approaches on three popular CSLR datasets: Chinese Sign Language dataset (CSL), PHOENIX-2014, and PHOENIX-2014-T. Experimental results show that our framework is effective.
Resilience of multi-object density fusion against cyber-attacks

Recently, it has been proposed to deal with fusion of multi-object densities exploiting the minimum information loss (MIL) rule, which has shown its superiority over generalized covariance intersection (GCI) fusion whenever sensor nodes have low detection probability. On the contrary, GCI shows better performance than MIL when dense clutter is involved in the measurements. In this paper, we are going to study the behavior of multi-object fusion with MIL and, respectively, GCI rules in the situation wherein the sensor network is exposed to cyber-attacks. Both theoretical and numerical analyses demonstrate that MIL is more robust than GCI fusion when the multi-sensor system is subject to a packet substitution attack.
Panoramic Image Correction of Borehole Inner Wall Based on Kirchhoff Circle Detection

Based on the problems of insufficient single visualization and waste of communication bandwidth when uploading images after collecting images in the existing downhole TV systems in the field of geological surveys, a method is proposed to remove the invalid areas of the collected panoramic images of the inner wall of the borehole, and expand and correct the valid areas into Method for the plan view of the inner wall of the drilled hole. First, use the Kirchhoff circle algorithm to detect the invalid black hole information area in the panoramic image of the inner wall of the borehole, and remove it after determining the limit; secondly, use the mathematical method of polar coordinates to delineate the ring limit of the effective area image and expand it into a Trapezoidal diagram; finally, the expanded trapezoidal image is stretched at four corners using the perspective transformation algorithm to restore the plan view of the inner wall of the borehole. Compared with the existing borehole optical imaging system, the present invention can effectively divide the effective area and the invalid area in the panoramic view of the inner wall of the borehole, save the communication bandwidth when uploading the image of the inner wall of the borehole, improve the collection efficiency, and has practical application. value.
Deep Learning Edge Detection in Image Inpainting

Deep learning-based image inpainting algorithm EdgeConnect was recently developed by K. Nazeri et al. Unlike traditional image inpainting methods, EdgeConnect uses the innovative edge-first and color-next approach. In this paper, EdgeConnect is improved to become a completely deep learning-based image inpainting method by using deep learning-based edge detection. The EdgeConnect and the modified EdgeConnect are both trained on the same datasets and the results are scored with the image quality assessment metrics including PSNR, SSIM, MAE and FID. The modified EdgeConnect in this paper demonstrates better learning efficiency and inpainting performance.
Manifold Similarity Reconstruction based Video Hashing

Most existing unsupervised video hashing methods are built on unidirectional models with less effective similarity preservation objectives, which underuse the bidirectional correlations among successive frames and are susceptible to noisy data while constructing the similarity matrix. To alleviate the problems above, in this paper, we propose a novel unsupervised video hashing method, called Manifold Similarity Reconstruction based Video Hashing (MSVH). Inspired by the MLM (mask language model) architecture, we design a visual reconstruction loss to fully exploit the bidirectional correlations between frames by forcing the model to extract information from context. Different from the previous researches, a novel similarity reconstruction loss is adopt considering both Euclidean distance and manifold structure to cope with noise. Furthermore, we develop a more reliable classification loss to exploit the global statistics of the whole dataset as a complement to local similarity preservation. Extensive experiments implemented on three public benchmark datasets, FCVID, UCF101 and HMDB51, show that the proposed method outperforms the state-of-the-art baselines.
Track Label and Classical and Quantum Probability Densities

We discuss if track labels are needed in a tracker using physics-based reasoning. Using the concept of de Broglie wavelength, we show that it is possible to label macroscopic objects and their trajectories, whereas it is not possible to assign labels to microscopic objects. A number of cases are examined to show the difficulty of not using track labels in multitarget tracking. We analyze a simple case of two identical macroscopic and microscopic objects to show that indistinguishable macroscopic objects do not exist. Use of track labels in real-time and tracker evaluation is also discussed.
A Keyphrase Extraction Method Based on Multi-feature Evaluation and Mask Mechanism

Keyphrase extraction aims to identify phrases in documents that contain core content. However, existing unsupervised keyphrase extraction models are limited to focusing on a single feature leading to biased results. In response to the above problems, it evaluates keyphrase scores through multiple features of semantic importance, topic diversity, and position features. Firstly, it masked the candidate keyphrase from a document and the Manhattan distance between the mask document and the original document is calculated as the semantic importance feature. Secondly, it calculated the topic-word distribution of candidate keyphrases as topic diversity, and the position features are calculated. Finally, the phrase importance score is calculated by integrating the three sub-models. Experiments are conducted on three academic datasets and compared with six state-of-the-art baseline models, outperforming existing methods. The results show that evaluating phrase importance from multiple features significantly improves the performance of extracting keyphrases.
Convolutional Recurrent Neural Networks with Attention Mechanism for Streaming QoE Prediction

Cloud performing arts businesses has been accelerated by the advent of the 5G era and the COVID-19 pandemic, so there is a growing demand for a quality of experience (QoE) predictive model. However, QoE is a time series factor with nonlinear relationship influence, including subjective and objective factors named Quality of Service(QoS), which leads to a high complex prediction. To solve this problem, existing studies have utilized Long Short-term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) to effectively capture this kind of complex dependency, respectively, to obtain excellent QoE prediction accuracy. However, they can not take into account the accuracy and computational efficiency at the same time. So we proposes CGRU-QoE, that is, using CNN to extract global information, using the variant of LSTM--Gate Recurrent Unit (GRU) to extract context information, and then following the Attention Mechanism. In addition, we introduced a new input factor representing bitrate. The proposed method is mainly validated in the LFOVIA database and is superior to the baseline method in terms of prediction accuracy and computational complexity.
A Faster implementation of Multi-sensor Generalized Labeled Multi-Bernoulli Filter

The recent multi-sensor Generalized Labeled Multi-Bernoulli (GLMB) is an efficient analytic implementation to the multi-sensor multi-object state estimation problem. The multi-sensor multi-object posterior is recursively propagated using the multi-sensor multi-object filtering density, by updating it with multi-sensor measurements at each time step. The measurement update step requires solving a series of NP-hard multidimensional assignment problems. In this paper, we introduce a faster implementation of this algorithm by an intuitive approximation, and combine that with the Gibbs sampler based truncation approach to produce an efficient multi-sensor multi-object estimation solution suitable for practical applications.
Multiple Step Ahead Forecasting of Rooftop Solar Power Based on a Novel Hybrid Model of CEEMDAN - Bidirectional LSTM Network with Structure Optimized by PSO Method

Improving the accuracy of solar power forecasting is becoming important as the development of this type of energy tends to increase sharply in the coming years. Forecasting solar power, especially rooftop solar power still faces many difficulties due to its dependence on solar radiation and other weather parameters. To deal with this problem and increase the forecasting performance, in this paper, a hybrid model was developed for short-term rooftop solar power forecasting. The developed hybrid model firstly omitted the white noise time series from the original dataset using the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method and the remainder of the dataset was the input of the forecasting model using Bidirectional Long short-term memory (BiLSTM). Then, particle swarm optimization (PSO) was employed to optimize kernel parameters and weighted coefficients in the forecasting model. To demonstrate the forecasting performance, the proposed method was applied to forecast the rooftop solar power in Ha Bao, Lam Dong city, Vietnam and compared with 3 other models. Four evaluation metrics namely RMSE, n-RMSE, sMAPE and MAE showed that the proposed model outperformed the compared models, thus, the proposed PSO-CEEMDAN-BiLSTM was an effective method for short-term rooftop solar power forecasting.
An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models

Recent development of the multi-sensor generalised labelled multi-Bernoulli (MS-GLMB) tracking algorithm allows joint estimation of target trajectories adjunct to clutter rate and detection probability. Nevertheless, it requires prior knowledge of new birth target distribution which might not be available in certain tracking scenarios. Conversely, another algorithm has been proposed to handle unknown birth statistics using multi-sensor measurement and a Gibbs sampler, but not be able to estimate clutter rate and detection probability. In this paper, we propose a multi-sensor multi-target tracking algorithm to handle unknown clutter rate, detection profile, and statistics of new birth targets. Our algorithm assumes linear Gaussian property on the dynamic and measurement models for closed-form analytic computation. Experiment with a 3-D tracking scenario demonstrates the robustness of our algorithm.
Revisiting the Bearings-only Filtering Problem

We analyze the filter initialization problem in the bearings-only filtering (BOF) problem of a non-maneuvering target for the scenario where real data is processed. We assume that the target moves with the nearly constant velocity motion in the XY−plane. The ownship, also moving in the XY−plane, performs a maneuver to observe the state of the target. The state of the target is a four-dimensional Cartesian state vector with 2D position and velocity components. Although the dynamic model for the relative Cartesian state vector is widely used for state estimation in the BOF problem, we argue that it is simpler and computationally efficient to use the absolute Cartesian state vector in place of the relative Cartesian state vector. The Cartesian unscented Kalman filter (CUKF), Cartesian cubature Kalman filter (CCKF), and Cartesian PF (CPF) are used in this study. Sensor technology and real-time signal processing algorithms are expected to improve significantly in future. We analyze the performance of these filters in the high measurement accuracy and high data-rate regime. Our results show that the state estimation accuracy of all the filters in this regime are nearly the same. Therefore, a simple filter such as the CCKF is suitable with a low computational cost.
An Image Caption Model Based on the Scene Graph and Semantic Prior Network

In this paper, we propose an image caption model based on scene graphs and semantic priors to address the problem of traditional image caption models that are overly dependent on training data. First, the original image features and the scene graph features are fused by embedding the scene image features into the feature space. Second, using image captions from the existing dataset, the sentence reconstruction task is used to train the memory network to retain semantic prior knowledge. The scene graph features are then combined with semantic prior information to reconstruct the new features, which are then sent into the Decoder to produce an image caption.