Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Performance Monitoring Scheme for Cloud Applications

With the rapid development of cloud technology, cloud applications have brought great convenience to people's production and life. Cloud applications rely on multiple microservices, and multiple applications sharing cloud platform resources will have an impact on each other's microservice performance. Cloud service providers may also tamper with microservice performance monitoring data out of interest, which poses a greater challenge to the reliability and security of cloud applications. Therefore, this paper proposes a blockchain-based cloud application performance monitoring scheme to improve the reliability and security of cloud application performance monitoring by implementing a secure aggregation of performance data from multiple microservices. The model uses homomorphic encryption algorithms to encrypt data and perform linear homomorphic hashing operations to ensure the security and integrity of the data; smart contract technology is used to securely aggregate ciphertexts, as well as decrypt and verify the aggregated data to achieve trusted computing and privacy protection of the performance monitoring data of microservices. Through experiments, the feasibility of our proposed model is demonstrated and the overhead of the blockchain-based system is tested.
Construction and Experimental Evaluation of Document Causality Extraction Model Based on CGCN-BERT

With the increasing degree of networking, the amount of document data also increases. The document causality extraction method is a new data regression analysis technology. In this paper, the Central Graph Convolutional Networks-Bidirectional Encoder Representation from Transformers (CGCN-BERT) text classifier was used to label and filter keywords. This approach can make it easy to extract and judge the causal relationship of documents. Therefore, this paper explored the construction method of document extraction model under this model. This paper mainly used the methods of statistical analysis and experimental comparison to study the document relationship extraction model of CGCN-BERT. The experimental data showed that in the three document tests, the average recall rate was 86.57%, but its accuracy was as low as 67.77%, and the relationship value G was 78.63%. Therefore, the relationship extraction model designed in this paper has some room for improvement.
Rogue key and impersonation attacks on FIDO2: From theory to practice

FIDO2 is becoming a defacto standard for passwordless authentication. Using FIDO2 and WebAuthn, web applications can enable users to associate cryptographic credentials to their profiles, and then rely on an external authenticator (e.g., a hardware token plugged into the USB port) to perform strong signature-based authentication when accessing their accounts. The security of FIDO2 has been theoretically validated, but these analyses follow the threat model adopted in the FIDO2 design and explicitly exclude some attack vectors as being out of scope. In this paper we show that two of these attacks, which appear to be folklore in the community, are actually straightforward to launch in practice (user PIN extraction, impersonation and rogue key registration). We demonstrate a deployment over vanilla Linux distributions and commercial FIDO2 authenticators. We discuss the potential impact of our results, which we believe will contribute to the improvement of future versions of the protocol.
A Survey of Steganography Tools at Layers 2-4 and HTTP

Steganography has evolved into various forms and remains an effective way to hide sensitive information. Network Steganography, also known as "Covert Channels," is popular in fields such as terrorism and security. As a result, the scientific community created a specific taxonomy to categorize it, and developed several techniques and tools to conceal communication between the parties. In this paper, we have curated a list of available software tools that can be used to create a covert channel at layers 2 to 4 of the ISO/OSI model and related to the HTTP protocol.
AI/ML-based real-time classification of Software Defined Networking traffic

One particular example of a useful software application for Software Defined Networks (SDN) is represented by a traffic analysis mechanism, which provides a network administrator with a control panel from which he can collect traffic data. The data can then be used to fit Artificial Intelligence (AI) models, which will further classify the traffic of the network in real-time, enabling a network admin to monitor the network with ease. This paper presents an SDN classifier, aiming to achieve real-time multi-class traffic classification in a software-defined network. To enhance the classification accuracy, six artificial intelligence algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machines (SVM), Decision Tree, and Artificial Neural Networks (ANN), are tested. Due to the possibility of training on unnormalized data, the data is preprocessed by rescaling values between 0 and 1. Additionally, the paper explores the supervised learning potential of the last three algorithms in traffic classification. The findings show that one of the top performing algorithms is ANN, along with SVM and KNN.
Cheaper than you thought? A dive into the darkweb market of cyber-crime products

The darkweb is nowadays considered a very popular place to sell and buy illegal cyber-crime related content. From botnet services and malware, to user data such as credit card information and passwords, darkweb marketplaces offer ease of use, product variety, and most importantly effective anonymity to both buyers and vendors. In this paper, we crawl 8 popular darkweb marketplaces and perform a comprehensive quantitative analysis with a focus on cyber-crime related products. Moreover, we report some preliminary findings when examining the same marketplaces through their I2P mirrors. Our results suggest that overall there is a multitude of products that fall into the cyber-crime category, with products under the Fraud category dominating the market, and that the average cyber-crime products’ price is relatively low. Furthermore, we explore how the vendors of this specific product group are distributed across platforms, utilizing harvested information such as usernames and PGP keys, and investigate how their reputation scores affect their operation.
Securing cloud-based military systems with Security Chaos Engineering and Artificial Intelligence

Recently, system security represents a big challenge for many organizations, and it must be specifically handled when a system is intended to be deployed in a cloud environment. Cloud environments provide multiple security services that run over a Shared Responsibility Model that requires the participation of the cloud provider and the customer. Thus, this paper proposes an architecture based on Artificial Intelligence to support the finding of system threats and errors in an early stage and on Security Chaos Engineering methodology to reliably test the existence of such errors. This proposed architecture may help orientate better system designs and contribute to building holistic security. A particular use case is described to show how the proposal can be applied to a system that supports services for a military-related organization.
Sentiment Analysis with Soft-Voting Method on Customer Reviews for Purchasing Transactions of E-Commerce

The accuracy of customer reviews is crucial for an e-commerce platform to assist buyers in selecting high-quality products from a vast array of options. This research aims to develop a sentiment analysis model for evaluating customer opinions expressed in e-commerce product reviews. The proposed approach utilizes the Soft Voting (SV) technique, which demonstrates superior accuracy compared to the conventional Sentiment Selector (SS) method. The sentiment analysis model’s accuracy is determined by gathering probability values from three classifiers (Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB)) for each sentiment category (positive, neutral, negative). The evaluation is conducted using the Tokopedia product review dataset. The findings indicate that the Soft Voting (SV) model outperforms the Sentiment Selector (SS) approach. The proposed SV model achieves an accuracy, precision, recall, and f1-score of 69%, 70%, 69%, and 69%, respectively.
Unified asymptotic distribution of subspace projectors in complex elliptically symmetric models

The statistical performance of subspace-based algorithms depends on the deterministic and stochastic statistical model of the noisy linear mixture of the data, the estimate of the projector, and the algorithm that estimates the parameters from the projector. This paper presents different circular and non-circular complex elliptically symmetric (CES) models of the data and different associated non-robust and robust covariance estimators whose asymptotic distributions are derived. This allows us to unify and complement the asymptotic distribution of subspace projectors adapted to these models and to prove several invariance properties that have impacts on the parameters to be estimated in CES data models.
Transparent Fundraiser for Projects Using Blockchain Technology

The evolving developments in blockchain technology have helped in deriving a path through which the community can achieve to develop a healthy platform that is transparent and trustworthy for both investors as well as startups. It is with the help of core logic for handling mechanisms and the brain of blockchain, also known as smart-contract that it utilizes in achieving this goal. The paper presents the implementation of such a logic which helps in achieving the solution to the issues faced by earlier-generation ICOs. The work involves the addition of an extra layer of a smart contract wherein the funds will be stored instead of going directly to the owner’s account. The money gets transferred only upon receiving majority votes from the investors on the withdrawal request made by the owner. This ensures the money is spent well and the owner doesn’t run away with all the stored money.
Reputation-based consensus mechanism for digital order transaction alliance chain

The digital order trading platform is a new e-commerce platform that allows digital assets to be traded. In this environment, multiple roles, digital assets, and physical transactions coexist, but there are challenges such as node dishonesty, oligopoly, and user inaction. To address these challenges, this article proposes a reputation-based consortium blockchain consensus mechanism. First, it uses a weighted combination of users’ reputation value, historical transaction and block success rate to measure their transaction reputation and allocates resources accordingly. Second, it prevents oligopoly and inaction by limiting the number of consecutive rounds for miner selection and diluting the reputation of inactive users. Third, it designs and implements the consensus algorithm and analyzes its efficiency, fairness, and feasibility. The results show that the reputation-based consortium blockchain consensus mechanism can improve the performance in terms of consensus efficiency, user dishonesty, monopoly, and inaction. It reduces the consensus time by about 30% compared to the PBFT algorithm and stabilizes the reputation of candidates after about 50 rounds of consensus. It meets the actual transaction needs of the new digital order trading platform and integrates multi-role supply chain markets, digital asset trading, and consortium blockchain technology.
Realization of Embedded Basketball Video Target Tracking Algorithm Based on DSP-FPGA

At present, when using the target tracking algorithm to track embedded basketball sports videos, due to the accumulation of errors, the tracking drift and the inability to deal with the occlusion of the target result in the long execution time of the target tracking algorithm and the small number of successfully tracked and marked targets. The realization of the embedded basketball video target tracking algorithm based on DSP-FPGA is a project aimed at developing an embedded system for basketball video target real-time tracking and analysis. This algorithm uses digital signal processor (DSP) based on FPGA and customized software and hardware components to achieve target tracking algorithm. The solution can be used in monitoring, sports, medical and military fields. This paper introduces the work of developing target tracking algorithm using DSP -FPGA platform.
The Evaluation Model of University Education Level Based on Deep Learning Algorithms

Evaluation is an important means to promote the development of college education. At present, scholars have carried out research on college education and have also proposed some evaluation indicators for college education. The purpose of this article is to study the educational level evaluation model based on deep learning algorithms. First, based on deep learning, the AGA-BP model is proposed, which outlines the concept of education level, builds an evaluation index system with output and input resources, and designs corresponding questionnaires to establish a university education level evaluation model based on deep learning algorithms. Taking Province M as an example, this model can provide powerful tool support for the practice of evaluating the educational level. The experimental results show that the lowest scoring higher education efficiency score reached 1.2 in 2018, and higher quality output can be obtained through reasonable adjustment of higher education input. Evaluation practice provides guidance and suggestions for the development of higher education level, and provides decision-making support for higher education.
An Efficient WBC Cancer Cells Prediction Using Dense Net Algorithm

The critical factor causing cancer disease is aberrant cells that prevent normal blood cells from growing. There are many types of cancer diseases. This study proposes automating the diagnosis of this condition. This study suggests the use of tiny blood pictures. There were 62 preparations and 38 tests. Segmentation and clustering were performed using YCbCr, Gaussian Distribution, Otsu Adaptive, and K-Means. Convolutional Neural Networks were used to classify GLCM features. This splits these disorders into two groups based on similar symptoms, making diagnosis challenging. The doctor had a choice between two possibilities. Each technique calculates the characteristics of one of the two sickness groups. The Random Forest classifier was used to conclude. The proposed method intends to increase system learning, reduce misdiagnosis, and detect white blood cell malignancy early. This study suggests using tiny white blood cell pictures to detect leukemia in patients. The cell shape classifier was investigated using MATLAB and LabVIEW in this study. The proposed method intends to increase system learning, reduce misdiagnosis, and detect white blood cell malignancy early. This study suggests using tiny white blood cell pictures to detect leukemia in patients. This research’s primary application is using machine learning prediction systems in hospitals.
Design of Multimodal Information Evaluation System Based on Unsupervised Learning

The 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century is an era of rapid technological development, with increasingly intelligent and personalized means of transmitting, storing, and displaying information. Human communication methods, media, and modes are constantly changing. Human beings can no longer be satisfied with simply communicating information through language or writing, but rather hope to use multiple modes for information reception and transmission. This paper proposed a multimodal information analysis method based on unsupervised learning algorithm. First, it explained and defined the relevant theories of multimodal information and unsupervised learning, and then carried out experimental analysis. The experimental results showed that the efficiency level of multimodal information analysis based on unsupervised learning was higher than that of traditional multimodal information analysis.
GSM Based Smart Irrigation System with Arduino UNO Powered by Solar Panel

In the past few years ago, the global rate of smart irrigation it is expected likely to make the market was worth at $1,404.4 million at the annual compound growth rate of 13.8% from between 2021 to 2030 and will reach to $4,495. This is attribute or assign to the ameliorate better Research and Development for growing and improving the activities and the progress of embrace and espouse of the ability to exert effort in energy-efficient smart irrigation systems there is a rapid and fast growth in the field of agriculture technology. In enlargement, excessive weather conditions are universal across the countries in the world wide web. Irrigation in such region must be extremely effective, thereby hustling the require for these systems. India is an agriculture country where farmers are the most important part. Farmers plays a very crucial role in overall productivity of our country. As we all know that agriculture is one of the most important root of surviving mass number of bulk Indians which has uncountable credit on economy of the country.
Hardware Acceleration of Pre-trained Networks for Colorectal Polyp Classification

The objective of the research is to identify a pre-trained Convolutional Neural Network (CNN) with faster inference time for classification of colorectal polyps on a Field Programmable Gate Array (FPGA) device. Basically, Deep Learning(DL) models are trained and executed using Graphical Processing Units (GPUs) having high processing capacity, but those devices consume more power during execution. DL models that are appropriate for real-time miniature instruments must be implemented with low-power consumption. This paper evaluates the performance of relevant polyp classification models using FPGA (Avnet Ultra96-V2) and GPU (Quadro RTX 5000). The CNN models are trained on GPU and then deployed for inference on FPGA using the VITIS-AI platform and PYNQ framework. The MobileNetV2 performs classification with 88% accuracy and least inference time of 2.60 seconds at a throughput of 176 frames per second on FPGA. A precise comparison of the throughput and power consumption during execution on GPU and FPGA are provided. The FPGA device is found to perform <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1}.\mathbf{25}\times$</tex> faster than GPU in terms of throughput while using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{40}\times$</tex> less power with a negligible reduction in accuracy.
Early Stage Ovarian Cancer Prediction using Machine Learning

The most dangerous cancer that affects women is ovarian cancer and the early-stage diagnosis is difficult. To overcome this issue, machine learning techniques being used to predict the early stage of ovarian cancer in women. This paper discusses the different features that link with the prediction of cancer through clinical data. The different machine learning algorithms, like logistic regression, support vector machines (SVM), and decision trees are the primary focus of the paper to predict cancer at the early stage. This paper focuses on the accuracy of different models like logistic regression, support vector machine, decision tree used to predict the early stage cancer. The paper discusses an integrated approach that uses random forest feature selection method and a random forest classifier to give more accurate results. The proposed model has accuracy of 91% as compared to the other models with accuracy 81%,84%,83% respectively.
EPD: an integrated modeling technique to classify BC

In the past two decades, Breast Cancer (BC) had found as second most common death and continues to be prone in low-middle income countries. However, in those days there have been a lot of technologies developed and implemented in the medical field so far but still unable to cure this disease completely. Thus, need to be more conscious and design novel techniques that would be able to avoid unnecessary deaths at the early stages. In this study, we have taken key studies of related cells, and risk factors and design a novel EPD (EDA, PCA, and DT) model to classify abnormal cells into either benign (B) or malignant (M). Furthermore, EPD has been designed by combining three major techniques as Exploratory Data Analysis (EDA) to visualize the raw data, principal component analysis (PCA) to select the most promising features, and Decision Tree to predict the disease with these features. These findings show the best novel approach against BC for doctors as well as healthcare organizations as compared to individual techniques.
Classification of Endangered Species of Wild Cats through Xception Transfer Learning Model

In recent years, populations of wild cats such as Caracals and African Leopards have dropped low enough that many wild cats have been labelled as endangered or critically endangered by the International Union of Conservation of Nature's Red List of Endangered Species. Caused due to direct or indirect human activities such as hunting and poaching, and industrialization leading to habitat destruction, it has become vital to monitor and manage these majestic creatures for their conservation. Identifying and classifying wild cats from images is a difficult feat that demands reliable and effective deep learning (DL) techniques. This work demonstrated a method for identifying and categorizing wild cats using 1439 photos with 224×224×3 pixel resolution and ten annotated classes, including African Leopard, Caracal, Cheetah, Clouded Leopard, Jaguar, Lion, Ocelot, Puma, Snow Leopard, and Tiger. The Xception transfer learning (TL) model, a Convolutional Neural Network (CNN) that has been pre-trained and has demonstrated outstanding performance on several image categorization tasks was employed. Using the TL strategy, the Xception model was optimized on the dataset of wild cat images. Data augmentation methods were also used to broaden the variety of the training dataset and enhance the model's generalizability, such as rotation, zooming, and horizontal flipping. The test findings demonstrate that the suggested approach successfully detects and classifies wild cats from the images with an accuracy of 96%, a sensitivity of 96%, and a specificity of 99.4%. The proposed methodology could be implemented to identify and monitor various wild cat species in their natural habitats for wildlife conservation, monitoring, and management.
Research on the Consistency of Foreign Table Structure Based on Foreign Data Wrapper

The federated database built by PostgreSQL through Foreign data wrapper, when the table structure parameters of the remote sub-database change, the foreign table of the central database cannot access the corresponding tables of the sub-database normally, which affects the data management and service of the central database. It is of great significance to study how to maintain the structure consistency between remote table and foreign table in real time for ensuring the high availability of foreign tables. In this paper, database service components with foreign table consistency is built through techniques such as transaction backtracking and event triggers for remote table structure changes. Experiments on several federated databases show that the proposed method can effectively provide the synchronization of remote table and foreign table structure, solve the consistency problem of federated database table, and realize the high availability of central database applications and services.