Reviewerly/exHarmony huggingface dataset:
Research on Text Detection Algorithm based on Improved FPN

In recent years, due to the increasingly application of machine vision in various aspects, the topic of text recognition in actual scenes has gradually become a research hot spot of machine vision research. For images with complex backgrounds, the first thing to do is to accurately locate the position of the target text, and then the text content can be efficiently identified. However, as far as the current text detection algorithms based on deep learning are concerned, there are still problems such as incomplete extraction of text feature regions, and wrong detection of images as text regions. Therefore, this paper proposes an improved DB algorithm to solve the problems of the variable shape and complex background of the text area in the label text detection task, so that the algorithm can achieve better detection effect and better performance in complex scenes. The content of the article is mainly from the following main aspects:firstly, the current situation of text detection algorithms is introduced, then the improvement of the DB algorithm with ResNet-50 as the backbone network is proposed, and finally mPA (mean Average Precision) is used as the evaluation of text detection. By comparing the various detection algorithms, it is found that the improved algorithm has significantly improved the detection accuracy and recall rate, and the model speed is also faster.
CapsNet on Embedded Devices in a Data Scarce Scenario

This paper explores the use of the Capsule Neural Network (CapsNet) on small datasets for image recognition and its possible embedded/edge device implementation. As compared to data-hungry Convolutional Neural Network (CNN), CapsNet can operate with data scarce scenarios. CapsNet is briefly presented, followed by the training process. A CapsNet model is designed and trained, exploring the effects of various hyperparameters on its performance. With a two-class dataset of 30 images per class, our proposed CapsNet model yields training and validation accuracies of 70% after training on 35 epochs, but begins to overfit if trained beyond. An equivalent baseline CNN model already overfits before 35 epochs, with training and validation accuracies of 99% and 66.70%, respectively. This shows that with small datasets, a CNN overfits rapidly regardless of the number of epochs, while we can design a CapsN et model that performs considerably better on small datasets, if properly tuned. We also show that a CapsNet model can execute onto an embedded/edge device after compression for reducing its size and complexity.
Tracking of Partially Visible Elliptical Objects with a Lidar Sensor using Random Matrices and a Virtual Measurement Model

Virtual measurement models (VMM) can be used to generate artificial measurements and emulate complex sensor models such as Lidar. The input of the VMM is an estimation and the output is the set of measurements this estimation would cause. A Kalman filter with extension estimation based on random matrices is used to filter mean and covariance of the real measurements. If these match the mean and covariance of the artificial measurements, then the given estimation is appropriate. The optimal input of the VMM is found using an adaptation algorithm. In this paper, the VMM approach is expanded for multi-extended object tracking where objects can be occluded and are only partially visible. The occlusion can be compensated if the extension estimation is performed for all objects together. The VMM now receives as input an estimation for the multi-object state and the output are the measurements that this multi-object state would cause.
Machine Learning. Stochastic Method of Solving the Problem of Classification with a supervised imbalanced training Set

In machine learning with a large class imbalance in a supervised training set, a small class can be poorly found. We use Anderson's discriminant class estimates of posterior class probabilities to solve the imbalance problem. We do not change the proportions of classes in the training set, but choose the cost of classification errors from the ranges calculated from the posterior probabilities at points in the training set of a small class. Thus, we solve the problem in the framework of the stochastic method.
Entropy Evaluation of Data Flows Using an Integrative Approach

We propose an integrative approach for modelling the symbolic information flows in information-analytical systems (IAS) and obtaining entropy characteristics for information management. We combine the entropy characteristics of the evaluated multimedia objects and the entropy estimates expert's opinions into a single algorithm for identification.
Improved U-Net Network Segmentation Method for Remote Sensing Image

Semantic segmentation and extraction based on remote sensing images has important theory and significance. Deep learning has become one of the mainstream methods to extract information from remote sensing images. In this paper, based on the improvement of U-Net network structure, we combine ASPP and skip connection. Improve the residual module to improve the information extraction method. The main improvements of this paper are: 1 Based on the U-Net network structure, we use the multi-scale feature detection capabilities of Pyramid to introduce. The ASPP module and the residual structure are improved, paying more attention to semantic and detail informatization, overcoming the limitations of U-Net in small target detection; 2 We have improved the U-Net network, using skip connections to get more layers of information. Experiments show that the model proposed in this paper has significantly higher MPA and MIOU than the U-Net model on both the VOC dataset and the Vaihingen dataset. It means that ARU-Net can extract information better.
Service Parameters Identification for Adaptive Networked Music Performance

Networked Music Performance (NMP) aims at revolutionizing musical interaction. NMP services need straight QoS (quality of service) requirements. So far, in best effort network, musicians adopt solution that try to reduce as much as possible the delay and jitter contributions under their control (i.e., at the peripheral part outside of the network). In this paper we will present a brief overview on NMP and some of its specific requirements that should be mapped on future networks able to provide adequate QoS performances such as the promised “deterministic Internet” or proper slicing parametrization provided by 5G cellular networks and beyond. The aim of the paper is to presents an in-depth-study of latency issue in this domain underlining the most significant blocks that contribute to the overall end-to-end latency. The proposal presented in the paper is to identify control parameters useful to more flexibly smooth the network requirements, discussing about some possible practical implementations. Lastly, a low-cost measurement set-up is described, and some preliminary experimental results are shown.
Research on Human Pose Estimation Algorithm Based on Two-Stream Residual Steps Network

Human pose estimation plays an important role in machine vision. 2D-based human pose estimation has received more attention due to its low 2D space complexity. However, many algorithm networks detect human key points in different environments, and their key-points prediction accuracy will decline to varying degrees. In order to solve the problem of weak generalization of human keypoints detection in different environments. First of all, this paper, based on the small influence of illumination in the MSR space image and the rich human detail information stored in the RGB space image, proposes a pose refinement network and a pose correction network to extract features from the two spatial images. Moreover, in order to better fuse the feature information extracted from the two complementary spaces of MSR and RGB, an adaptive feature fusion algorithm is proposed. Finally, keypoints are regressed by generating a heatmap. The experimental results show that the network has high prediction accuracy on both MS COCO and MPII datasets, and can maintain good generalization in different external environments.
Virtual Assistants to bring geospatial information closer to a smart citizen

This article presents a proposal for the use of virtual assistants integrated into a Spatial Data Infrastructure (SDI), which will facilitate citizen's access to available resources in creating geospatial information scenarios, without the need for increased knowledge or technical training. The chatbot would also allow access to SDI technology to be transparent to the citizen, allowing access to different map servers at the same time. The scenarios created refer to the main events registered in the National Risk and Emergency Management Service (SNGRE) of the country. The existing information is very data rich insomuch that the ordinary citizen does not know how to access its source. This project takes the initial steps for the integration of various sources of geospatial information with the goal to archive a core of geospatial information for services of City Intelligent. Ideally, this new form of access to geospatial information will also allow decision makers, using natural language, to have additional information to improve territorial planning and land use processes.
Semi-supervised Malicious Traffic Detection with Improved Wasserstein Generative Adversarial Network with Gradient Penalty

With the development of artificial intelligence, malicious traffic detection technology based on deep learning has become mainstream with its powerful detection performance. Most existing deep learning-based detection methods require sufficient labeled data to train classifiers. But much labeled traffic is difficult to obtain in practical applications. To solve this problem, we propose and implement a semi-supervised malicious traffic detection method based on improved Wasserstein Generative Adversarial Network with Gradient Penalized (WGAN-GP), denoted as SEMI-WGAN-GP. First, we construct a pseudo- feature map (PFM) for each stream in the dataset using the time-series properties of consecutive packets in a given stream. Second, we fix the generator and only train the discriminator on a few labeled PFMs, which obtain a discriminator that can distinguish malicious from benign traffic. Finally, the generator and discriminator are trained unsupervisedly in the adversarial setting, which allows the discriminator to improve detection performance by generator-generated PFMs. Experiments on the publicly available UNSW-NB15 dataset demonstrate that SEMI-WGAN-GP can achieve 90.53% accuracy using a few labeled samples (20% of the samples in the dataset are marked), exceeding the 79.92% and 84.94% of fully supervised multilayer perceptron network (MLP) and 2- dimensional convolutional neural network (2DCNN). In addition, SEMI-WGAN-GP also achieves better detection performance than SEMI-DCGAN by generating better samples.
Planning the Operations of a Group of Autonomous Unmanned Underwater Vehicles Based on a Carrier Vessel

We propose a method for calculating the work carried out as part of the monitoring of a river or water basin by a group of autonomous unmanned underwater vehicles (AUV) accompanied by a base vessel. This method is based on a two-level model. We give examples of monitoring a linear object (pipeline or cable) and randomly located inspection objects.
Sparse Broad Learning System via a Novel Competitive Swarm Optimizer

Broad Learning System is recently proposed intended as an alternative to deep learning neural networks. It has attracted a lot of attention due to its excellent performance on classification and regression problems. However, the typical BLS is based on grid search to determine the hidden layer nodes, which undoubtedly imposes a heavy training burden. On the other hand, BLS chooses to use sparse autoencoder to fine-tune the weights of input data to feature mapping nodes as a way to reduce the uncertainty caused by random mapping and extract sparse features. In contrast, this paper proposes a BLS that determines the hidden layer nodes and sparse hidden layer weights by solving a multi-objective optimization problem. Firstly, we propose a novel competitive swarm optimizer(NCSO) with embedded sparse operators, called S-NCSO. Second, a bi-objective optimization problem with an error cost and the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> norm of the hidden layer weights is solved by using S-NCSO to determine both the hidden layer nodes and the sparse hidden layer weights. Finally, experiments on multiple regression datasets show that the proposed method not only yields sparser weights and compact structures, but also greatly reduces the training time while improving the prediction accuracy.
Open Networking for Modern Data Centers Infrastructures: VXLAN Proof-of-Concept Emulation using LNV and EVPN under Cumulus Linux

Modern telecom service providers need modern Data Centers infrastructures. One Overlay encapsulation protocol brings the ease, flexibility, and good-response behavior to support technological changes oriented to support customer requirements: “VXLAN”. This work presents a VXLAN proof-of-concept emulation using the Open Networking paradigm and confirm the mentioned characteristics of the protocol through two mechanisms: The not-so-common process called LNV (Lightweight Network Virtualization) and EVPN (Ethernet VPN) for large scale implementations, bringing scalable and low-cost but high-performance solutions.
Federated Learning Using Three-Operator ADMM

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and have direct access to rich datasets. In this paper, we argue that leveraging the rich data on the edge server is much more beneficial than utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33\% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.
A Hybrid Intelligent Clustering Model for Tackling Incomplete Mixed Data Using Heuristic Algorithm with Artificial Intelligence

Recently, in various information areas including image processing and social networks, data clustering has been vastly utilized. Thus, the clustering task is taken as successful while imputing the quality of uncertain values, whereas the traditional methods observe only poor performance. To enhance the performance, the hybrid intelligent-based clustering model is proposed by the adaptive concept. Initially, the data is collected from benchmark datasets that are composed of incomplete mixed data. The first level of clustering is processed by optimized K-Means Clustering (KMC) to acquire the optimal centroid, in which the centroid is optimized by the Adaptive Probability-assisted Water Strider Algorithm (AP-WSA). Similarly, the second clustering is done through K-Correlation-based Clustering (KCC) to obtain the clustered output. Thus, the final clustering process is named as Hybrid K-Means K-Correlation (HKMKC) clustering algorithm. Finally, the experimental results are validated with the performance measures and estimated with the traditional methods. Through the result analysis, the Average Silhouette Coefficient (AS) of the designed AP-WSA-HKMKC is 50%, 28%, 16%, and 5.45% progressed than KMC, FCM, KCC, and K-median clustering methods for dataset 2 at 15% miss rate. Thus, the designed hybrid clustering ensures the efficiency of handling incomplete data regarding accurate clustered outputs.
BIM Driven Retrofitting Design Evaluation of Building Facades

Building facades are components that shape a structure's daylighting, energy use, and view factors. This paper presents an approach that enables designers to understand the impact that different facade designs will have over time and space in the built environment through a BIM-enabled augmented reality system. The system permits the examination of a range of facade retrofit scenarios and visualizes the daylighting simulations and aesthetics of a structure while retaining function and comfort. A focus of our study was to measure how participants make decisions within the multi-objective decision space designers often face when buildings undergo retrofitting. This process often requires designers to search for a set of alternatives that represent the optimal solution. We analyze the decision-making process of forty-four subjects to determine how they explore design choices. Our results indicate the feasibility of using BIM-enabled AR to improve how designers make informed decisions.
AdapTables: Using Conformal Mapping for Collaboration on Tables in Asymmetric Mixed Reality

Remote meeting applications are becoming more immersive by supporting virtual reality, however, support for augmented reality devices is still lingering. For augmented reality to be integrated, the asymmetry between the local spaces of the users needs to be solved, to which we contribute by focusing on the mismatch between tables. We present the AdapTables system, which maps a virtual reality user's virtual meeting table onto an augmented reality user's differently shaped physical table. By creating conformal maps between these tables, remote users can be transported to the environment of the other user. Additionally, shared virtual objects are also mapped to adapt to each user's table. We tested the system with 32 participants in pairs of two.
Coexistence Studies for DV-QKD Integration in Deployed RAN Infrastructure

We present a practical quantum/classical multiplexing scheme in a mobile x-haul optical transport segment deployed in a fiber pair. Photon counting measurements are reported, based on which a performance evaluation of the COW-QKD protocol is presented and a secret key distillation rate at 1.43 kbps is calculated.
A Comparative Analysis of Various Loss Functions for Image Data Hiding Using Deep Neural Network

In today’s highly emerging and dynamic world, data and information plays a major part in computer communication and the global economy. The improvement in the computing and information sector has caused a serious threat to the data. Also, the increased use of internet has made security as a fact of major concern. The data that is transmitted in our day-to-day life may be attacked by the hacker. To handle these kinds of problems, one of the potential solutions is the data hiding technique. Among such techniques, data hiding in an image is the most commonly used one. Also, embedding a secret content in an image will be a rigid task for the third party to decrypt the information. Data hiding can be done more effectively with a number of AI and deep learning concepts. This paper presents a comparative study of the various loss functions for image data hiding using deep neural network. It evaluates the performance metrics of various loss functions using visual quality metrics.
K-Means and BIRCH: A Comparative Analysis Study

With the rise of the application of machine learning in academia and industrial sector, clustering has become an important field of study. Clustering has been extensively used in studies involving unlabeled data, image processing, and unsupervised learning. The purpose of this article is to discuss various applications of clustering, and take up two clustering algorithms of K means and balanced iterative reducing and clustering through hierarchy clustering (BIRCH), as these algorithms have been applied to a wide array of studies across different domains, and document a comparative study between these two. K-Means is a technique of partitioning-based clustering, whereas BIRCH clustering is a technique of hierarchical method of clustering. The methodology is based on comparing BIRCH and K-Means clustering algorithms, using a total of five datasets. The preprocessing of dataset is described in this article; encoding is performed on different attributes of these datasets and dimensionality reduction using principle component analysis is performed. In this article, validation of clustering performance using the internal validation is done, and here silhouette index is used. The silhouette index takes into account the mean distance between the clusters. The results are tested on 2–100 clusters, and deduction of optimal quantity of clusters is done as well. On every dataset, it is observed that K-Means outperforms BIRCH clustering method. Thus, we come to the conclusion that the K-Means clustering algorithm proves out to be better for clustering for our considered datasets.
Active-sensing-based decentralized control of autonomous mobile agents for quick and smooth collision avoidance

There is an increasing demand for multi-agent systems in which each mobile agent, such as a robot in a warehouse or a flying drone, moves toward its destination while avoiding other agents. Although several control schemes for collision avoidance have been proposed, they cannot achieve quick and safe movement with minimal acceleration and deceleration. To address this, we developed a decentralized control scheme that involves modifying the social force model, a model of pedestrian dynamics, and successfully realized quick, smooth, and safe movement. However, each agent had to observe many nearby agents and predict their future motion; that is, unnecessary sensing and calculations were required for each agent. In this study, we addressed this issue by introducing active sensing. In this control scheme, an index referred to as the "collision risk level" is defined, and the observation range of each agent is actively controlled on this basis. Through simulations, we demonstrated that the proposed control scheme works reasonably while reducing unnecessary sensing and calculations.