Reviewerly/exHarmony huggingface dataset:
Domain Adaptation for Network Performance Modeling with and without Labeled Data

Network performance modeling using machine learning (ML) has proven to be essential for proactive network and service management. Dynamic changes and re-configurations in the network infrastructure can impact the data distribution, causing degradation in the performance of the deployed ML models. To adapt to such changes, traditional approaches suggest that the models must be updated using new labeled data from the operational network. Unfortunately, these labels can be costly or impossible to obtain.In this paper, we propose a customized approach named cDANN using domain adaptation to target these challenges for network performance modeling. We report on an empirical study where we evaluate the impact of labeled data availability, ranging from no available labels to a fully labeled data set. We specifically study scenarios for network performance modeling using two realistic data sets; one obtained from a cloud testbed environment and the other from a 5G - mm Wave testbed. Our results show that incorporating unlabeled samples reduces the need for costly collection of labeled data. The proposed method either radically improves or performs on par with the baselines.
HA-SARSD: An Effective SAR Ship detector via the Hybrid Attention Residual Module

The all-day and all-weather characteristics of the synthetic aperture radar (SAR) images make them be widely applied in the maritime monitoring field. Recently, convolution neural networks-based (CNNs) SAR ship detection algorithms are hot research topics. However, owing to the indistinctive ship features and complex backgrounds, outstanding feature extraction ability is required. Moreover, it is challenging to balance the detection effect and the inference speed. Therefore, a novel hybrid attention-synthetic aperture radar ships detector (HA-SARSD) based on the You Only Look Once version 5 (YOLOv5) is proposed in this paper. The local hybrid attention residual module (LHARM) is designed to optimize the feature extraction ability. Owing to the abundant channels in the deep-level feature, LHARM is developed in the fifth layer of HA-SARSD. Experimental results on Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) and SSDD datasets show that HA-SARSD optimizes the SAR ship feature extraction ability and obtains the balance of detection effect and speed.
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution

Generative models learned from training using deep learning methods can be used as priors in under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image super-resolution approaches.
Group-Wise Feature Fusion R-CNN for Dual-Polarization SAR Ship Detection

Ship detection in synthetic aperture radar (SAR) images is a hot pot in the remote sensing (RS) field. However, most existing deep learning (DL)-based methods only focus on the single-polarization SAR ship detection without leveraging the rich dual-polarization SAR features, which poses a huge obstacle to the further model performance improvement. One problem for solution is how to fully excavate polarization characteristics using a convolution neural network (CNN). To address the above problem, we propose a novel group-wise feature fusion R-CNN (GWFF R-CNN) for dual-polarization SAR ship detection. Different from raw Faster R-CNN, GWFF R-CNN embeds a group-wise feature fusion module (GWFF module) into the subnetwork of Faster R-CNN, which enables group-wise feature fusion between polarization features and multi-scale ship features. Finally, the experiments on the dual-polarization SAR ship detection dataset (DSSDD) demonstrate that GWFF R-CNN can yield a ~4.1 F1 improvement and a ~2.9 average precision (AP) improvement, compared with Faster R-CNN.
On the NCA Versus NCV Models in Tracking Maneuvering Targets

When tracking maneuvering targets with a nearly constant velocity (NCV) Kalman filter with discrete white noise acceleration (DWNA) or a nearly constant acceleration (NCA) Kalman filter with discrete Wiener process acceleration (DWPA), the selection of the process noise variance is complicated by the fact that the process noise errors are modeled as zero-mean white Gaussian, while target maneuvers are deterministic or highly correlated in time. In recent years, for the NCV Kalman filters with DWNA, the deterministic tracking index was introduced and used to develop a relationship between the anticipated maximum acceleration of the target and the process noise variance that minimizes the maximum mean squared error (MaxMSE) in position. A lower bound on the process noise variance was also expressed in terms of the maximum acceleration and deterministic tracking index. Recently, the design methods for NCV Kalman filters with DWNA were extended to develop design methods for NCA Kalman filters with DWPA for tracking maneuvering targets, and the effectiveness of the design methods were illustrated via Monte Carlo simulations. However, the question of when to use an NCA filter instead of an NCV filter for tracking maneuvering targets remained unanswered. In this paper, this question is addressed utilizing these two design methods to achieve a fair comparison of two filters that are optimally designed.
Adaptation of Multi-target Tracker Using Neural Networks in Drone Surveillance Radar

In this paper, we present a study on adapting a radar multi-target tracker using neural networks to enhance its performance against agile, maneuvering, targets such as drones. In particular, a network dynamically adjusts the process noise of the target motion model based on the normalised filtering innovations. Different neural networks are evaluated for this task, namely fully connected, recurrent and convolutional networks. They are trained on representative simulated data, including waypoint-driven trajectories which are common with (semi-) autonomous systems, e.g. small unmanned air systems. Results from synthetic radar data demonstrate the potential benefits of adapting a multi-target tracker with a low-complexity recurrent neural network, albeit the modest improvements it achieves.
LSTM Framework for Classification of Radar and Communications Signals

Although radar and communications signal classification are usually treated separately, they share similar characteristics, and methods applied in one domain can be potentially applied in the other. We propose a simple and unified scheme for the classification of radar and communications signals using Long Short-Term Memory (LSTM) neural networks. This proposal provides an improvement of the state of the art on radar signals where LSTM models are starting to be applied within schemes of higher complexity. To date, there is no standard public dataset for radar signals. Therefore, we propose DeepRadar2022 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a radar dataset used in our systematic evaluations that is available publicly and will facilitate a standard comparison between methods.
Road Navigator Identification using Deep Learning Techniques

Road Navigator Identification (RNI) is a necessary aspect of modern safety systems for drivers and vehicles. Traffic signs are the key to navigation. The boards help us to supervise the Route. Our system is capable of detecting the road condition and goal information. In automated self-driving cars, the classification of road signs is crucial. Most of the Research works on Traffic sign Detection did not provide real time solution yet. Despite of some disturbances, Deep Learning methods are able to provide good accuracy in Traffic sign Recognition. The system is designed to identify key characteristics in images of traffic signals and categorize them accordingly. The described technique identifies traffic signs by generating a deep model of them using a modified neural network. The German Traffic Sign dataset is used for detecting and classifying the Traffic Signs and gives us good results with an accuracy of 98% which is better than previous research works.
Mining Twitter for Insights into ChatGPT Sentiment: A Machine Learning Approach

In the past few years, ChatGPT has evolved into a powerful N.L.P. technology, with applications ranging from text generation to question resolution. However, there is still relatively little research on how the public perceives this technology. In this research, we use sentiment analysis techniques to assess the sentiment of tweets regarding ChatGPT. Users manually categorized a dataset of tweets mentioning ChatGPT as positive, negative, or indifferent based on their attitude. The overall sentiment of the tweets was therefore directly determined utilizing machine learning models including logistic regression and support vector machines. Our results show that the majority of tweets related to ChatGPT are neutral, while a smaller proportion are positive or negative. We also found that certain words and phrases, such as "AI" and "language model", are strongly associated with positive sentiment, while others, such as "bias" and "privacy", are associated with negative sentiment. These findings have important implications for the development and deployment of ChatGPT and other NLP technologies, as they suggest that public perception is influenced by factors such as trust, transparency, and ethical considerations. Overall, this paper highlights the importance of understanding public sentiment towards emerging technologies like ChatGPT, and the potential of sentiment analysis techniques to shed light on these issues
Novel Approach to Record the Attendance of Students Using Facial Recognition

During ancient Indian times, the Gurukul system of education was the style of learning in the country. In this system the students were learning with their mentors (Gurus) and receiving education, knowledge, moral values and life skills under the guidance of their gurus. This system of education was practiced in ancient times, where all students who resided at the place of the guru in the Gurukula were considered equal. As the days passed the things took a drastic change and government schools were introduced. A few decades ago, the number of students in government schools decreased due to the privatization of the education system. Later, more schools were introduced by corporations, which led to a significant increase in the number of schools. To perform the tasks like monitoring and taking the attendance of each class was tedious job at the same time it was time consuming, where the total number of students in each class has increased and number of subjects for each class also increased and every teacher has to document the number of students attending the classes for each subject and they need to submit it to the higher authorities. To overcome these difficulties, In this paper a novel approach is schemed to record attendance of all the students of a class. In the proposed system to take the attendance of the students all at once, a live video is processed for each frame and to recognize the faces of all the students it uses a deep convolutional neural network face recognition algorithm which extract 128-dimensional face encodings from images and then compares these encodings with the faces stored in the dataset to determine the best match and further the attendance of the students present in the class is recorded in the form of excel sheet so that the teacher can carry out the further analysis. The findings of the experiment overcomes the difficulties faced in the existing systems and eyewitnesses the furturistic transition in marking attendance.
Neural Network Algorithm Based on Particle Swarm Optimization and Design of Immersive Intelligent English Teaching in College English Teaching System

With the development of intelligent technology and the continuous improvement of the requirements of the information society for English teaching, the traditional artificial model can no longer meet the personalized and efficient learning of students. In order to enable students to concentrate in the learning process, this paper uses the neural network (NN) algorithm of particle swarm optimization (PSO) to design the immersion intelligent English teaching (IET) system. This paper mainly uses the method of experimental comparison and optimized NN algorithm to model the teaching system. The experimental data shows that the mean square error is maintained below 0.3. The new data sample can be used to analyze a person’s English level through a trained NN. In recent years, with the continuous progress and innovation of technology, immersive intelligent English teaching has been widely applied and studied in the field of college English education. Combining particle swarm optimization and neural network algorithms to achieve more accurate and efficient model training and optimization. Immersive intelligent English teaching refers to using virtual reality technology and other means to immerse students in the English context, improving learning effectiveness and learning experience. The design of this teaching model needs to integrate multiple disciplines, such as pedagogy, psychology, computer science, and so on.
Bangladeshi Traffic Sign Recognition and Classification using CNN with Different Kinds of Transfer Learning through a new (BTSRB) Dataset

An accident is the cry of a lifetime. The role of traffic signs is most important in preventing or mitigating accidents. In many cases, while driving, traffic signs are not visible in the conventional approach; if traffic sign detection and recognition can alert the driver of important sign guidance ahead, it will help in accident mitigation. Traffic sign recognition plays a monumental role in expert systems, such as traffic assistance driving systems and automatic driving systems. The prime purpose of this paper is to design and identify a computer-based system that can spontaneously detect the direction of a road sign. For this research work, we have created our own dataset, which is called the Bangladeshi Traffic Sign Recognition Benchmark (BTSRB) dataset. The dataset, BTSRB, was created by capturing images from different angles and under different parameters and conditions. A total of 7320 images were collected to create this comprehensive database. This dataset called BTSRB all the images collected from Bangladesh. In this paper, we used five different types of models (CNN, Inception V3, MobileNetV2, ResNet50, and VGG16), which are pre-trained on the ImageNet dataset. Later, we finetuned the pre-trained model and used transfer learning. The main challenge of this research is collecting datasets from a country like Bangladesh, where no recognized dataset is available. When compared to another model, the accuracy of this model is greater than 91%. This paper emphasizes the significance of traffic sign recognition in expert systems and the necessity for a well-established dataset in nations where such resources are not readily available.
Restoration Method of Motion Blurred Image Based on Feature Fusion and Particle Swarm Optimization Algorithm

The current computer situation is that people generally have home computers. With the rise and prosperity of the Internet era such as short video, people have higher and higher requirements for high-definition status of pictures and videos. In addition, some people hope to use Internet technology to repair and clarify ancient photos and sports videos. Therefore, in order to make the motion blurred image clearer, this paper studies the image restoration method with the help of feature fusion and particle swarm optimization algorithm. In this paper, we mainly use analytic hierarchy process (AHP) and experimental case analysis and comparison to restore blurred images in different motion states. The experimental results show that the maximum time difference of the proposed method for processing motion blurred images can be 13.1s, which is much smaller than the other two methods.
Discriminative Deep Association Learning based on the Optimized Feature Analysis Adaptive Spider Foraging Model for twitter sentiment analysis

The sentiment aspect is an important discussion in social media to discuss various forums like the product, social events, monuments, etc. All over, social media is the most dominant factor in discussion forums user comments as tweets be problematic analyses. Due to increasing sarcasm in social media terms contain sentiment terms and behaviors of users, the importance of features in data analyses needs more deep evaluation to improve the accuracy. To propose a Discriminative Deep Association Learning based on the Optimized Feature analysis Adaptive Spider Foraging Model (ASFM) to predict the occurrence of the event in social media terms. The method utilizes the tweets and messages generated from a social network with Tweet term features. Initially, the progress begins with the preprocessing of the social media terms and Tweet term facts to identify the features. Because of the sentimental side of sarcasm, the Semantic Entropy Vector Transformation model detects both sarcasm and non-sarcasm weights as features. Social foraging models identify optimal features based on fitness weights. The tweets and the structure of tweet words are analyzed and grouped into classes based on a semantic ontology process.
The Smart Development of the Large Scale Sensing Techniques in Intelligent Industrial Automation

Intelligent industrial automation is becoming increasingly popular, as companies look to automate processes and maximize efficiency. One way they are achieving this is through the use of large scale sensing techniques. These techniques enable companies to monitor the performance of their equipment and make adjustments if necessary. Large scale sensing techniques are used in industrial automation to detect physical quantities such as temperature, pressure, flow, and vibration. By using sensors, companies can detect problems before they become serious issues. This allows them to make changes and keep their machines running as efficiently as possible. Large scale sensing techniques are also used to monitor the environment in which machines are operating. This can help companies identify potential risks, such as high temperatures or humidity, and take the necessary measures to avoid damage. It can also help companies make better decisions about how to deploy their resources to maximize efficiency. Lastly, large scale sensing techniques can be used for predictive maintenance. By monitoring the performance of machines, companies can detect potential problems early on and take preventive measures before they become serious. This helps to avoid costly repairs and downtime, and ensures that companies are able to keep their machines running smoothly.
The Smart Development of Maximum Distance Rendezvous Point Model For Commercial Scheduling of Complex Networks

The maximum distance rendezvous point for commercial scheduling is a point at which two or more commercial flights can meet for transfer of passengers and/or cargo. This rendezvous point is usually located in an area that is geographically convenient and cost-effective for both the airline and the customer. In order to determine the maximum distance rendezvous point, the airlines must consider a variety of factors, such as the distance between the airports, the availability of ground transportation to and from the airport, the number of passengers and cargo being transferred, the number of flights arriving and departing, the cost of fuel, the safety of the airspace, and the weather. The rendezvous point must also be able to accommodate the size of the aircraft involved, as well as the number of passengers and cargo that need to be transferred. This can be accomplished by having a designated area close to the airport that is large enough to accommodate the aircraft, as well as any necessary ground transportation. The rendezvous point should also be located in an area that is free of congestion and in a safe airspace. In addition, the area should have adequate security measures in place to ensure passenger and cargo safety. This can include security checkpoints, as well as trained personnel to monitor the area.
Segment-Based, User-Generated Image Styling with Neural Style Transfer

With the recent improvement in machine learning and deep learning technologies due to an increase in computation power, its uses for image processing and computer vision have also increased. The style of the image, through orthodox methods, has been changed with the help of filters that are not capable of applying the style from only a few sets of options, with no consideration for semantic or contextual data. The transfer of style is also limited to the entire image, with no provisions for applying style to only a selected portion of it. Through advanced deep learning technologies, style transfer can be achieved with semantic and contextual accuracy while also providing the ability to apply to a selected portion of the image. The style can also be generated through the use of images generated for the text using deep learning.
Advanced Machine Learning Detection using UAV Motion Tracking and Control

Drones, or unmanned aerial vehicles (UAVs), have become more commonplace thanks to the booming commercial UAV sector. The potential for these devices to do significant harm, whether on purpose or by accident, has prompted urgent security concerns. In recent years, several solutions have been presented by academics and businesses to the problem of protecting vital infrastructure. Because to its superior resilience, computer vision is often employed as an autonomous drone detection solution over other offered alternatives such as RADAR, acoustics, and RF signal analysis. As deep learning algorithms are among the most efficient of these computer vision-based methods, they are increasingly being used in practise. In this work, we offer a self-sufficient drone tracking and identification system that makes use of a stationary beam profile and a lesser camera on a movable turret. In order to make the most optimal use of both resources, we suggest a combined multi-frame machine learning detection approach, where the frame from the zoomed turret camera is superimposed on the frame from the wide-angle static camera. With this method, we are capable of building a cost-effective pipeline that allows for the simultaneous identification of tiny sized aerial invaders on both the main picture plane and the magnified image plane. In addition, we provide the whole system, which includes tracking techniques, deep learning classification frameworks, and protocols.