Reviewerly/exHarmony huggingface dataset:
A Network Traffic Classification Method Based on Federated Learning and Extreme Learning Machine

As one of the important research areas in the field of network security, network traffic classification has been receiving a lot of attention. At present, machine learning has been widely used in network traffic classification and achieved good results. However, the network traffic classification method based on machine learning has some problems, such as isolated data island, lack of labeled data and data leakage. Therefore, this paper proposes a network traffic classification method based on Federated learning and Extreme Learning Machine (NTFLELM). NTFLELM does not need to aggregate data for centralized operation, and only needs to pass model parameters, that is, it can use multiple local models to train global models cooperatively, which effectively solves the problems of isolated data island and the lack of labeled data.At the same time, it protects data privacy by sharing only model parameters but not specific data. The NTFLELM is evaluated on the KDD CUP99 data set, and the experimental results show that compared with the benchmark algorithms, the accuracy of NTFLELM network traffic classification is improved by 12%.
A data augmentation strategy for scene text recognition

Synthetic datasets alleviate the shortage of label data in the real world to some extent. But, synthetic datasets still have problems with the complexity of picture backgrounds and text diversity. It is well known that collecting large amounts of real data is a job that requires a lot of human resources and material resources. Therefore, we propose a small batch data augmentation strategy, hoping to improve significant performance by collecting and labeling small batches of real data. We have verified our ideas on a strong baseline. The result shows that the accuracy of the model can be significantly improved by replacing the synthetic dataset with the real dataset, which proved that real datasets could train the model better than synthetic datasets. Then, we use different enhancement strategies to expand the data of small batches of real data sets and explore the performance improvement of the model under the condition of low-resource real data. Finally, we mixed the augmented small batch of real datasets and synthetic datasets to make the model learn the image features of real scenes more elegantly. The results show that the proposed strategy can well fill the gap between synthetic and real datasets and improve the model performance.
Design and Implementation of Horse Riding Action Monitoring Platform Based on Deep Learning

In order to meet the increasing demand of video analysis and its related applications, the method of temporal motion detection based on deep learning has attracted more and more attention. Through the motion detection system, users can use the labeled data for supervised training through the training module of the system to generate a deep learning model for extracting the target semantics, and can also call the model through the prediction module of the system to calculate and analyze the silhouette of the moving person in the video. Accurate analysis of sports training posture by technical means is one of the important means to improve people's competitive level in modern sports. The method adopted in this paper is deep learning. CNN training model is applied to spatial stream (video image) and temporal stream (dense optical stream) respectively to judge the scores of the categories of actions, and different methods are explored to fuse the scores of the two networks to get the final classification results. According to the research in this paper, this method is effective and can be widely used.
Performance Evaluation for Deploying Dockerized Web Application on AWS, GCP, and Azure

Cloud computing is a growing technology widely used by companies, academic institutions, governments, and personnel. Each of these entities utilizes cloud computing for different purposes and requires different cloud service specifications. Entities require a test to determine the performance of cloud computing services for selecting the correct solution to the problem. In this research, tests are carried out using a web application on a Docker Container environment deployed on the three cloud computing services. JMeter, SysBench, and Apache Benchmark are used to get an evaluation performance test result. Stress test results using the Jmeter show that AWS has the fastest time for request execution and the highest number of threads executed in a second. For Sysbench testing, there are several assessment parameters used. For read-only and read-write data parameters on databases, Azure excels compared to GCP and AWS. in the CPU performance test parameter, AWS gets the highest score compared to GCP and Azure. For IO file performance testing, GCP is the best compared to AWS and Azure. For memory testing, Azure excels compared to GCP and AWS. In the Apache Benchmark test for API load testing, GCP and Azure get results that are not significantly different. However, Azure is slightly better on average than GCP and AWS.
A self-supervised, few-shot semantic segmentation study based on mobileViT model structure

Vision tasks such as semantic segmentation face challenges due to a lack of data and the need to reduce model parameters. In this study, we present a novel approach that unites several techniques, including the mobileViT model, and self-supervised training, to improve the performance of few-shot image segmentation. Our approach leverages unsupervised saliency estimation to obtain a pseudo-mask and contrast learning to enhance the feature representation. Furthermore, we introduce the MaskSplit method to divide up saliency masks into the artificial query and support sets. To train the few-shot segmentation model, we apply self-supervised meta-learning based on vision transformer. Importantly, our approach eliminates the need for manual segmentation of annotations during training, enabling a fully self-supervised learning setup. We evaluate the effectiveness of our approach on two popular benchmark datasets for few-shot semantic segmentation, namely Pascal-5i and COCO-20i. Our study demonstrates its effectiveness in small-sample semantic segmentation tasks.
An Improved YOLO-v4 Algorithm for Recognition and Detection of Underwater Small Targets

Since the underwater complex environmental conditions seriously affect the accuracy of underwater target recognition, in view of the low accuracy of traditional image recognition algorithms for target recognition and the lack of robustness in extracting features, an image enhancement method for small underwater targets is proposed. A scheme combined with the YOLO-v4 improved algorithm. Firstly, the K-means++ algorithm is used to improve the original input anchor box mechanism to achieve accurate anchor box selection, and the image data enhancement strategy based on geometric and photometric transformation is used to optimize the training network. Finally, the EIOU loss function is used to optimize the model performance and improve the loss function convergence speed. The experimental results show that the average accuracy rate of the improved network model reaches 98.37%, and the number of frames per second of image transmission reaches 35.69FPS, which is 4.4.% higher than the original algorithm.
Design and implementation of a new online English learning artificial intelligence system based on JAVA

The rapid development of computer information technology has had a profound impact on various industries and fields of society. In recent years, a large number of artificial intelligence software for computer-assisted teaching have emerged. In order to improve the efficiency of auxiliary software development and deployment, this paper uses the combination of Java and artificial intelligence language Prolog to develop a new software system that can realize online intelligent learning. The system also applies the idea of artificial intelligence expert system theory, which has certain creativity. Use Struts+ Spring+ Hibernate lightweight JavaEE framework to build. The system greatly reduces the degree of dislocation between various modules, which is beneficial to the expansion of future functions. In general, compared with ordinary teaching assistance systems, this system applies the artificial intelligence module combined with the forgetting law of the brain into the system, and designs a system for reviewing English knowledge points to check for omissions.
Ground classification based on optimal random forest model

Today mobile robots are widely used in medical, rescue, and service industries, and are gradually replacing labor costs and playing an increasingly important role in various industries. The correct recognition of ground types can help robots adapt to different environments and help them reach the target area efficiently and quickly. Random forest model is a classifier model with widespread applications and is often used to solve the ground classification problem of mobile robots. In response to the problem that the traditional random forest algorithm has many initial parameters and lacks a definite formula to obtain the optimal combination of parameters, which leads to the low accuracy of the traditional random forest classifier model. In order to extend the ground recognition method and improve the accuracy of the random forest model, this paper proposes a random forest classifier model optimized by using genetic algorithm to classify ground types. The experimental results show that the effect achieved by the optimized random forest model, in terms of accuracy, has a significant improvement in accuracy compared with the traditional algorithm, reaching a recognition accuracy of 93%.
Optimizing CNN Using Adaptive Moment Estimation for Image Recognition

Deep learning technology has been widely used in many applications. Especially in the field of image recognition, where convolutional neural networks (CNNs) have demonstrated superior performance. However, most existing papers focus on a particular model or algorithm, and lack a complete discussion on the whole process of CNN construction, training and usage. In this paper, the authors introduce a CNN optimized by Adaptive Moment Estimation (Adam) optimizer from the theoretical level to practical applications. The authors describe its concept, algorithm, model construction, model training, simulation, testing methods, and typical cases. Adam is an extension of the stochastic gradient descent algorithm, which has recently been actively used in the field of deep learning. The dataset contains two classes, aircraft and lake, with 700 photos in each category. The authors built a convolutional neural network model and trained it by using the Adam optimizer. Experimental results demonstrate that the trained network can correctly classify the images.
Radio Frequency Emitter Identification Based on Ensemble Learning

In order to accurately identify and authenticate radio frequency (RF) emitters, preventing the occurrence of problems such as device cloning, replay attacks and user identity impersonation, an identification method based on time- frequency feature extraction and ensemble learning is proposed. First, the short-time Fourier transform (STFT) is applied on the time-domain signals to generate the time-frequency images which carry the essential characteristics of the signals. Then three sub-models of Resnet34, Inceptionv3 and Densenet121 are trained on the data set with the features extracted by each sub- model and fused to obtain a comprehensive model. The experimental results show that the overall recognition accuracy of the proposed algorithm for 10 LoRa RF emitters outperforms the sub-models applied individually.
Research on Human Activity Recognition Based on Random Forest Classifier

The current machine learning algorithms classify human activities with inaccurate accuracy, poor generalization ability of the model, and poor classification effect. Proposing to use Random Forest classifier to classify the samples. The classifier has the advantage of good generalization ability, high accuracy, and the ability to handle a large number of sample data. After comparing with BP neural networks, Naive Bayesian networks, and decision trees, the random forest classifier achieved an accuracy of 98% on the test set, much higher than the 55%, 87%, and 88% of the other algorithms. In addition, we also tested the generalization ability of the model using the K-Folder cross-validation method, which yielded an average accuracy of 95.5%, also much higher than the 49.9%, 85%, and 90% of the other classifiers. The experimental results show that the random forest classifier has significantly improved in accuracy and generalization ability compared with BP neural network by 78% and 91%, respectively. Therefore, the superiority of the random forest classifier in human activity classification is proved.
An Approach to the Presumptive Detection of Road Incidents in Cuenca, Ecuador Using the Data from the Social Media Twitter and Spanish Natural Language Processing

The road situation in Ecuador, specifically in the city of Cuenca and its surroundings, can vary according to various aspects related mainly to the weather, or traffic incidents caused due to reckless actions by drivers. Therefore, many times these problems can cause some drivers to be forced to delay their trip or look for alternative routes to reach their destination, others can plan their departure according to the related news they find when browsing social media, which can be somewhat time-consuming and even unlikely to find. For this reason, in this paper, a mobile application is presented whose primary function is to collect publications from social media Twitter from different Twitter accounts that are related to road incidents and to classify them using Natural Language Processing (NLP) and Machine Learning (ML) models such as BERT. Then, we present an approximate location of the presumptive incident on an interactive map which allows each of these tweets to be displayed with a personalized icon that can indicate different types of road incidents and, at the same time, the details of the event that occurred. Two main experiments were carried out to find (a) the model accuracy, where a value of 80% was obtained as a result, which is considered a positive result for road incident classification, and (b) the execution of acceptance tests, where a questionnaire based on the ISO 9126 standard was presented to a group of bikers belonging to the city of Cuenca, obtaining as a result that for most users the application is new (85.8%), efficient (85.7%) and easy to use (64.3%). This research makes it possible to present news of road incidents in a centralized manner, which makes it easier for drivers to stay informed and thus avoid annoying interruptions in their circulation.
Online Training in Present-Day Conditions: Opportunities and Prospects

Education and training are moving to an online format. More and more educational service providers are offering a variety of online learning activities: online lectures and online courses, online marathons and intensive courses, online trainings, web conferences, webinars, etc. Organizationally, online training courses may be the greatest challenge. Online trainings are different from traditional ones. Therefore, many unresolved issues arise in practice. First, not all classroom training topics are suitable for distance learning. Second, time and space need to be organized differently. Third, group work organization may be rather tricky. Fourth, online training provides for no physical or emotional contact, which may be a challenge for trainers. These issues have received no theoretical substantiation yet. The author describes possible ways to solve the above problems based on practicing trainers’ experience. A goal of this study was to identify opportunities and prospects of online training in present-day conditions. The tasks of the study were as follows: to identify challenging aspects of planning and conducting online training sessions; to analyze online training opportunities and advantages; and to outline future development prospects of online training.
Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection

Graph Neural Networks (GNNs) are specialized neural networks that operate on graph-structured data, utilizing the connections between nodes to learn and process information. To achieve optimal performance, GNNs require the automatic selection of the best loss and optimization functions, which allows the model to adapt to the unique features of the dataset being used. This eliminates the need for manual selection, saving time and minimizing the requirement for domain-specific knowledge. The automatic selection of loss and optimization functions is a crucial factor in achieving state-of-the-art results when training GNNs. In this study, we trained Graph Convolutional Networks (GCNs) and Graph Attention Networks (GAT) models for node classification on three benchmark datasets. To automatically select the best loss and optimization functions, we utilized performance metrics. We implemented a learning rate scheduler to adjust the learning rate based on the model’s performance, which led to improved results. We evaluated the model’s performance using multiple metrics and reported the best loss function and performance metric, enabling users to compare its performance to other models. Our approach achieved state-of-the-art results, highlighting the importance of selecting the appropriate loss and optimizer functions. Additionally, we developed a real-time visualization of the GCN model during training, providing users with a detailed understanding of the model’s behavior. Overall, this study provides a comprehensive understanding of GNNs and their application to graph-structured data, with a specific focus on real-time visualization of GNN behavior during training.
Building and Testing a Network of Social Trust in an Underground Forum: Robust Connections and Overlapping Criminal Domains

Underground markets support e-crime by providing a place where merchants and buyers trade assets for a price utilizing various digital currencies, payment providers, and wallets. The anonymity of these marketplaces and incentives to avoid penalties for criminal activity create significant challenges in studying trust in these ecosystems. Underground forums are clearinghouses where deals can be arranged, and services can be identified as vendors and customers engage. Such forums may be open and do not clear transactions, nonetheless still offer opportunities for entry, entrepreneurship, and customer or product discovery, serving as critical intermediaries for the marketplaces and enabling new entrants to establish trust and actors in one market to reach out to another.The empirical evaluation of interactions in such forums illuminates how collaborative networks form, interact, socialize, and exchange knowledge. To contribute to understanding online crime, we offer an empirical analysis of an underground forum. Specifically, we examine interactions in the social network as a whole and those components of the network that support three major types of crime: traditional crimes that occur away from keyboards, transitional crimes that have both offline and online instantiations, and entirely online new crimes. We compare and contrast the network structure of these three types and document the interactions between their social networks. The results suggest that although communities follow the small world effect, identifying and removing highly connected moderators or prolific contributors will not harm any of these three communities or the network, unless a significant portion of the network is removed. By further observing the structural patterns, we find that transitional crime actors tend to cluster more compared to the other two crimes while having the highest density.
Prediction of Maneuvering Status for Aerial Vehicles Using Supervised Learning Methods

Aerial vehicles follow a guided approach based on Latitude, Longitude, and Altitude. This information can be used for calculating the status of maneuvering for the aerial vehicles along the line of trajectory. This is a binary classification problem, and machine learning can be leveraged for solving such problem. In this chapter, we present a methodology for deriving maneuvering status and its prediction using Linear, Distance Metric, Discriminant Analysis, and Boosting Ensemble supervised learning methods. We provide various metrics along the line in the Results section that give condensed comparison of the appropriate algorithm for prediction of the maneuvering status.
A versatile interaction framework for robot programming based on hand gestures and poses

This paper proposes a framework for industrial and collaborative robot programming based on the integration of hand gestures and poses. The framework allows operators to control the robot via both End-Effector (EE) and joint movements and to transfer compound shapes accurately to the robot. Seventeen hand gestures, which cover the position and orientation controls of the robotic EE and other auxiliary operations, are designed according to cognitive psychology. Gestures are classified by a deep neural network, which is pre-trained for two-hand pose estimation and fine-tuned on a custom dataset, achieving a test accuracy of 99%. The index finger’s pointing direction and the hand’s orientation are extracted via 3D hand pose estimation to indicate the robotic EE’s moving direction and orientation, respectively. The number of stretched fingers is detected via two-hand pose estimation to represent decimal digits for selecting robot joints and inputting numbers. Finally, we integrate these three manners seamlessly to form a programming framework. We conducted two interaction experiments. The reaction time of the proposed hand gestures in indicating randomly given instructions is significantly less than that of other gesture sets, such as American Sign Language (ASL). The accuracy of our method in compound shape reconstruction is much better than that of hand movement trajectory-based methods, and the operating time is comparable with that of teach pendants.
A Taxonomy of Radiofrequency Jamming and Spoofing Strategies and Criminal Motives

This article provides a taxonomy of radiofrequency (RF) jamming and spoofing tactics and strategies associated with specific criminal objectives. This is based on the fact that the motivations and strategies of cyber-attackers – predominantly financial – is well-known, but the motivations behind specific electromagnetic interferences is lacking in the current literature. Previous research has also overlooked the motivations of other actors using jamming and spoofing devices. The objective is to identify the most desirable spoofing and jamming strategies, likely targets, and likely motivations. The article finds that (a) previous literature on the subject overlooks a number of non-state actor motives, and therefore this research aims to fill this gap in knowledge; (b) out of 8 actors identified, denial of service attacks (7 out of 8), as well as so-called decoy spoofing (6), trojan spoofing (5), and jamming-enabled crime (5) are the most desirable strategies utilized; (c) out of 11 strategies identified, grey and black hat hackers (11 out of 11), terrorists (11) and activists (8), are likely to take advantage of most identified.