Reviewerly/exHarmony huggingface dataset:
Exploring the Performance of VGG16 and Efficient Net Models for Plant Disease Classification: A Comparative Approach

Crop production and food security are both seriously threatened by plant diseases. Convolutional neural networks (CNNs) have gained popularity as a potential tool for automating the process of identifying plant diseases thanks to the development of deep learning techniques. In this study, we investigate the use of the VGG16 and Efficient Net CNN architectures for plant disease classification and compare their results. The research uses a massive dataset that includes many types of plants and diseases. To prepare the dataset for further analysis, we add annotations to the photos and split the data into a training set, a validation set, and a test set. Using transfer learning and the ImageNet-obtained pre-trained weights, the VGG16 and Efficient Net models are trained on the same dataset. Accuracy is only one of the measures we use to assess the models' efficacy. In terms of plant disease categorization, both VGG16 and Efficient Net show very good accuracy in experiments. However, they perform in notably different ways. With a training accuracy of 96.06% and a Testing accuracy of 95.84%, Efficient Net surpasses VGG16, as shown by our comparison research. The Efficient Net model outperforms VGG16 in terms of accuracy while using many fewer parameters and being much simpler to run. These results suggest that Efficient Net is a better tool for identifying and classifying plant diseases. Finally, this paper presents a complete analysis contrasting the VGG16 model with the Efficient Net model for identifying plant diseases. The findings demonstrate that Efficient Net outperforms other similar methods and that this has practical implications for detecting plant diseases in outside environments.
Optimization Model of Housing Construction Project Scheduling Based on Optimized Ant Colony Algorithm

Construction management is a standardized means of project management. The construction project takes a long time and has many links, and the construction workers involved are complicated. Poor quality buildings will not only affect the construction enterprises’ brand, but also threaten the safety of people's lives and property. In this paper, an optimization model of housing construction project scheduling based on ant colony algorithm (ACA) is put forward. By studying the relationship among various sub-projects in the construction system of building projects, the management of building projects can be better realized. The results show that compared with the other two algorithms, the optimized ACA has certain advantages in recall and accuracy. There are many contents involved in the building construction process, and there are many uncontrollable factors in the building process, which greatly impact the project's construction quality. The construction industry can use this technology to strengthen the building construction management, solve the problems existing in the construction management, and improve the construction quality
Blockchain Based Secure Cross Layer Design for Wireless Sensor Networks

This report outlines the comprehensive and innovative design for data transmission in wireless sensor networks (WSNs) that leverages the power of blockchain technology and CLD (Cross Layer Design) to enhance security, scalability, energy efficiency, and the lifespan of the network. The proposed cross-layer design integrates blockchain at the cross-layer layer to provide secure and decentralized data storage, communication, and processing. By implementing blockchain, the proposed design addresses key challenges in WSNs such as node compromise, data integrity, and scalability. Simulation results demonstrate the effectiveness of the proposed design in improving security and performance compared to traditional WSN protocols. The goal of this research is to enhance the performance of WSN by incorporating a threshold base decision-making equation and addressing the issue of overhearing. The proposed method manages to adjust the broadcast strength based on the location of neighboring nodes. It also deals with issues generated because of blockchain, by optimizing the interactions between different layers, by optimizing the communication protocols and reducing the amount of redundant data transmission. Cross layer design can improve the utilization of available bandwidth and reduce the chances of network congestion.
One Way to Compensate for Missing Values in Datasets

Management of complex organizational systems is based on artificial intelligence models such as neural networks, genetic algorithms, and big data analysis techniques for identifying complex patterns. Many real-world datasets may contain missing values for various reasons. Training a model with a dataset that has a lot of missing values can drastically impact the model’s quality. The article considers an approach to missing data recovery based on Chebotarev’s method
Metrics and Indicators of Security of Critical Resources in State and Corporate Objects and Processes

The paper comprises development of methods for the formation and analysis of various metrics and numerical indicators for the operational assessment of security of critical resources in their application to state and corporate objects and processes. These methods take into account the structure of these resources, modes and operating conditions. The purpose of the study lies in a mathematical and methodological analysis of metrics and security indicators that can provide reliable and operational security assessments of complex managed information and telecommunications objects and processes.
Recurrent Neural Network on Revolutionizing Multimedia Systems and Empowering User Experience With Dynamic Content Delivery

In recent years, the rapid growth of multimedia content and the increasing demand for personalized user experiences have fueled the need for innovative approaches to enhance content delivery and design in multimedia systems. In this paper, the recurrent neural networks (RNNs) revolutionize multimedia systems, enabling dynamic content delivery and interactive design to empower user experiences. By leveraging their ability to capture sequential patterns, RNNs facilitate the analysis, understanding, and generation of dynamic multimedia content. By learning from user interactions and preferences, RNN models can effectively deliver tailored content recommendations, enhancing user engagement and satisfaction. Through the use of techniques like recurrent generative models, RNNs can generate novel and realistic multimedia content, including images, videos, and audio. Furthermore, RNNs enable interactive design in multimedia systems. By incorporating user feedback and interactions, RNN-based models can adapt and dynamically adjust the content delivery and design, providing an immersive and personalized experience for users. In conclusion, a transformative impact of RNN on multimedia systems, revolutionizing content delivery, and empowering user experiences. By leveraging RNNs, the multimedia systems can adapt and evolve to meet the growing demands of users, ushering in a new era of immersive and engaging multimedia experiences.
Optical Flow based method for Violence Detection

Securing human lives is of utmost importance in modern society. To ensure security and safety, nowadays, video surveillance is used everywhere starting from private residences to banks, ATMs, railway stations, airports etc. Although these cameras are useful tool to deter criminal activity, they act as mere witnesses in the worst case. It becomes a tough job for security and police personnel to patrol on streets and monitor many CCTVs at the same time. Sometimes, due to human error, crimes may go unnoticed. At this backdrop, this paper proposes an easily implementable violence detection system that turns CCTVs into smart cameras. The proposed system uses optical flow to detect violence and attempts to improve accuracy in real-world scenarios. The system can be modified to alert operators that allows law enforcement personnel to approach the crime scene immediately and take necessary action.
Sa-Paraformer: Non-Autoregressive End-To-End Speaker-Attributed ASR

Joint modeling of multi-speaker ASR and speaker diarization has recently shown promising results in speaker-attributed automatic speech recognition (SA-ASR). Although being able to obtain state-of-the-art (SOTA) performance, most of the studies are based on an autoregressive (AR) decoder which generates tokens one-by-one and results in a large real-time factor (RTF). To speed up inference, we introduce a recently proposed non-autoregressive model Paraformer as an acoustic model in the SA-ASR model. Paraformer uses a single-step decoder to enable parallel generation, obtaining comparable performance to the SOTA AR transformer models. Besides, we propose a speaker-filling strategy to reduce speaker identification errors and adopt an inter-CTC strategy to enhance the encoder’s ability in acoustic modeling. Experiments on the AliMeeting corpus show that our model outperforms the cascaded SA-ASR model by a 6.1% relative speaker-dependent character error rate (SD-CER) reduction on the test set. Moreover, our model achieves a comparable SD-CER of 34.8% with only 1/10 RTF compared with the SOTA joint AR SA-ASR model.
Zero-Knowledge Proof and its Application in Distributed Registry Systems

Existing distributed ledger systems ensure the integrity and availability of information, but they lack built-in mechanisms to ensure the confidentiality of transaction data recorded in the ledger. Any intruder can obtain all transaction data by downloading a copy of the ledger and trace the relationships between transactions and accounts. Thus, additional mechanisms are needed to ensure the confidentiality of transactions. One solution to this problem is to use a zero-knowledge proof protocol. This article discusses the use of zero-knowledge proof to ensure the confidentiality of transactions in distributed ledger systems. We consider both the UTXO-model and the account model.
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control

Learning for control in repeated tasks allows for well-designed experiments to gather the most useful data. We consider the setting in which we use a data-driven controller that does not have access to the true system dynamics. Rather, the controller uses inferred dynamics based on the available information. In order to acquire data that is beneficial for this controller, we present an experimental design approach that leverages the current data to improve expected control performance. We focus on the setting in which inference on the unknown dynamics is performed using Gaussian processes. Gaussian processes not only provide uncertainty quantification but also allow us to leverage structures inherit to Gaussian random variables. Through this structure, we design experiments via gradient descent on the expected control performance with respect to the experiment input. In particular, we focus on a chance-constrained minimum expected time control problem. Numerical demonstrations of our approach indicate our experimental design outperforms relevant benchmarks.
Blockchain Based Abstract Syntax Tree to Detect Vulnerability in IOT-Enabled Smart Contract

As blockchain technology and the Internet of Things (IoT) have advanced, smart contracts have found new uses in many industries. Ethereum has become the go-to system for smart contract development. However, major financial losses have come from the ubiquity of smart contract vulnerabilities. Existing techniques have struggled to make efficient use of the syntax information in contract source code, making the detection of these vulnerabilities a key field of research. The reentrancy flaw in smart contracts is addressed by proposing a new vulnerability detection tool called SC-Defender . The abstract syntax tree representation of the source code for a smart contract written in the Solidity language is the core focus of SCDefender 's deep learning technique. Its efficient vulnerability identification is the result of its use of a tree-based convolutional neural network. In order to remove superfluous branches while keeping the essential ones, we develop a pruning technique for abstract syntax trees. The vulnerability detection findings achieved by SC-Defender are encouraging, with a precision of 81.43 percent, a recall of 92.12 percent, and an F1-score of 86.4 five percent. The results of the abolition experiments show that the abstract syntax tree pruning technique is a crucial part of SC-Defender 's ability to find vulnerabilities. These improvements in smart contract vulnerability detection, especially in the context of IoT-enabled smart contracts, have a lot of potential to make smart contracts safer and reduce the economic risks that come with their broad use.
Spectral Investigation of Pseudo-Random Sequences: Token Analysis

The paper discusses graphical research methods of stochastic transformations. The modified methods for detecting anomalies, patterns and deviations in pseudorandom sequences are Described. Spectral tests for assessing the quality of stochastic transformations are presented. Proofs of correctness of the described methods are given. The results of PRNG research with proposed methods are presented. The recommendations to increase the effectiveness of proposed investigation methods are formulated.
Precision Unleashed: Enhancing Vital Sign Predictions Using Long-Short-Term Memory Networks

Precise vital sign prediction is essential to monitor and treat patients’ medical conditions. This paper provides a deep learning-based method using Long-Short-Term Memory (LSTM) networks to forecast the values of vital signs, particularly the body temperature and heart rate. To conduct a comparative study, the performance of the proposed LSTM model is compared to other prediction models, such as statistics and deep neural network models. The models’ performance assessment was performed using two vital sign datasets: the body temperature dataset from a publicly accessible dataset and the heart rate dataset from the MIT-BIH Arrhythmia Database. To determine how accurately the proposed model predictions were made, three evaluation measures are considered: mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results illustrate that the proposed LSTM-based model outperforms the industry standard models in decreasing the prediction errors for heart rate and body temperature. The results of this study demonstrate how well LSTM networks capture temporal connections and accurately predict vital sign readings. Besides, the proposed strategy attains the potential boosting of patient monitoring systems and the standard of healthcare delivery.
Automated Forensic Analysis Following Memory Content Using Volatility Framework

Volatility, a widely recognized open-source framework in the field of digital forensics, is specifically designed to extract and analyze volatile memory (RAM) from live systems. This powerful tool enables the examination of diverse memory artifacts, encompassing process activity, network connections, registry keys, and file system interactions. However, one limitation of volatility lies in its inability to determine the normalcy of processes. To address this gap, we have developed an additional component as an add-on to augment the framework’s capabilities. In this research paper, we propose a comprehensive framework for conducting forensic analysis of attacks, focusing on the behavioral aspects of processes. Our framework involves capturing a memory snapshot and subsequently comparing the behavior of processes during normal operation with their behavior when an attack occurs. By employing this method, we can discern anomalies, identify potential attacks, and gather valuable information about the attackers. Through our proposed framework, we have successfully achieved the identification of attacks and obtained crucial insights into the attacker’s activities.
Developing a Program for the Digital Learning Environment of Higher Education: Text Recognition Using the Tesseract Artificial Intelligence Technology

This article presents a program for the digital learning environment of universities: text recognition developed using the Tesseract artificial intelligence technology. An algorithm for recognizing text from pictures, photographs and drawings is presented. This development is implemented and used on the GPAYALTA Portal.
Strategies and Methods of Overcoming Threats to Information Security in Social Educational Networks

The article analyzes the didactic potential of using social networks as components of electronic information and educational systems of educational institutions. It is noted that social educational networks are increasingly subject to hacking attacks and are a source of information security threats. It is given the nomenclature of potential threats, as well as their characteristics. It is proposed a set of protection methods against threats that can be undertaken individually by the technical services of educational institutions. The advantages and disadvantages of each method are considered. They are given the examples of software products which use would reduce the risks of information security in social networks. Most of these tools are currently either little known or not available on the market. The creation and development of such products is supposed a promising task.
Unsupervised Texture Image Segmentation Based on Local Features

The article is devoted to the problem of unsupervised segmentation of texture images. Texture segmentation can be used in the analysis of satellite, geological, medical, biological images. Texture segmentation algorithms have many advantages: lightness, speed, the possibility of unsupervised implementation, simpleness of automatic labeling of data sets, a set of target tasks. Model with the following steps was chosen to solution. After a simple pre-processing of the raw image, the local vector texture characteristics of the each point of the image are calculated. For this goal used a bank of convolutional Gabor filters and empirical curvelet filters that produce frequency splitting. Computed local histogram aggregation. Then, clustering is performed based on the construction of a feature matrix and singular decomposition. All levels of segmentation automation up to fully automatic have been explored. All algorithms are implemented and tested on prepared synthetic and real data.
Decision Trees in the Selection of Electric Vehicles Based on Various Parameters

The production of electric vehicles is highly increasing in recent days to meet the demands of alternatives to fuel-based vehicles. The automobile industries are manufacturing new variants of electric vehicles which are both economical and environmentally friendly. As the number of electric vehicles is many in the market it is quite challenging for customers to make decisions on selecting electric vehicles and also the retailers of E-vehicles are confronted with the same challenges of decision making. This paper proposes a decision-making model for selecting E-vehicles by using the machine-learning technique of Decision Trees. The decision-making on selection is based on significant parameters. The data on different E-vehicles with respect to the core parameters are subjected to treatment with the supervised learning technique of Decision Trees in the R environment. In this case, the decision trees are used to classify the E-vehicles into the two categories of feasible and infeasible based on five decision parameters. On comparing the accuracy results of classification with other machine learning techniques, decision trees are found to be more optimal.
Circumnavigation Control of Non-Holonomic Vehicle System with Distance-Rate Measurements

This article studies the circumnavigation problem of a non-holonomic vehicle independent of the global coordinate. Specifically, the vehicle circumnavigates an unknown stationary target with anticipated distance and velocity. However, the position information (i.e., absolute position and positive position) is not available to the vehicle. Instead, the vehicle can just obtain the distance rate relative to the target. We propose a generic continuous and bounded control algorithm based on only the distance-rate measurement. It is demonstrated using Poincare-Bendixon Criterion that the proposed control algorithm in this article ensures the asymptotically stable circumnavigation. Simulation is finally given to verify the effectiveness of the proposed control algorithm.
Intelligent Analysis of Big Data in the Information Flow

The article considers the use of AI methods for creation of a knowledge base in the field of recreation using a national interactive map of local cultural trends. The map contains thousands of brands classified into 9 categories covering all the regions of the country.
Unlocking the Potential of Immersive VR Systems: The Role of Gesture and Natural Controls

As virtual reality technology continues to advance, immersive systems combined with gesture control are becoming increasingly prevalent and offer numerous benefits over traditional 2D interfaces. A recent system-level comparative analysis showed, that in a 3D immersive virtual environment, 31 percent fewer elementary operations were needed to reach to complete the same task workflows than in a 2D interface. Also, the information access cost was 54% less. The present paper aims to shed light on the factors which allow for lower complexity in immersive environments-especially the role of gestures, and natural controls. This paper analyzes and compares the interactions in 2D interfaces, desktop VR and immersive VR. Our objective was to comprehend the distinctions and their impact on user experiences by investigating the types of interactors utilized, the implementation of interactions, and the feedback provided in these environments.
An efficient fraud detection mechanism based on machine learning and blockchain technology

With fraud becoming more sophisticated, conventional detection methods are no longer effective, resulting in a worldwide impact on customers and organizations. To tackle this, cutting-edge technologies like machine learning and blockchain are being utilized by several institutions. This article assesses the efficacy of XGBoost, KNN, CatBoost, and Random Forest in detecting real-time fraud during financial transactions. Additionally, the paper discusses how blockchain technology can create a secure and tamper-proof database for financial transactions used in fraud detection. Our proposed financial fraud detection approach was analyzed using the "Synthetic data from a financial payment system," revealing that 98.79% of the dataset comprised genuine transactions, while 1.212% were fraudulent. The results showed that CatBoost had the highest accuracy rate, exceeding 99.46%, while Random Forest had the lowest accuracy rate of 98.31% among all algorithms. A machine learning and blockchain technique has finally been proposed to identify fraudulent bank transactions.