Reviewerly/exHarmony huggingface dataset:
A New Era of Web Mining: Innovative Approaches in Focused Web Crawling for Domain-Specific Information

In an era characterized by the exponential growth and complexity of the World Wide Web, this paper underscores the paramount importance of innovative focused web crawling techniques in enhancing domain-specific information retrieval. The study delves into the intricate development and optimization of specialized web crawlers, adept at navigating through the vast, intricate digital landscape to extract and collate information that is not only accurate but also highly pertinent to specific domains or themes. By employing advanced algorithms and methodologies, these refined crawlers address the prevailing challenges and intricacies, offering a more targeted, efficient, and effective means of data extraction. This paper provides an in-depth analysis of the major conceptual, technical, and practical strides, unveiling a new epoch in web mining that promises enhanced precision and relevance in information retrieval.
Optimizing Cloud Environments: Machine Learning-Driven Virtual Machine Migration Strategies

The management and delivery of IT resources has been completely transformed by cloud computing, which offers end users and enterprises scalable and adaptable solutions. In cloud systems, load balancing, strong fault tolerance, and resource optimisation all heavily depend on virtual machine (VM) migration. New paradigms for virtual machine (VM) distribution have been created by recent advances in machine learning (ML) heuristics, allowing for the dynamic and intelligent allocation of computing resources. In this study, we conduct a comprehensive examination of the heuristic uses of machine learning for migrating cloud-based virtual machines.
Multi-view Classifier and Fast Brain Tumor Segmentation Using Geometric Fast Data Density Functional Transform

The article proposed a new architecture for fast brain tumor image segmentation based on their intrinsic geometric properties using the geometric fast data density functional transform (g-fDDFT). The g-fDDFT, as a feature pre-extractor, utilized the structure of AutoEncoder to provide global convolutions on images for extracting global features. It also exploited the geometric properties of energy landscapes offered by functional operations to reduce the computational costs caused by convolutional operations efficiently. Under the g-fDDFT framework, the computational complexity would reduce from $\mathcal{O}(n^3)$ to $\mathcal{O}(n\,\text{log}\, n)$. To verify its performance on tasks of brain tumor image segmentation and the recognition of oriented views of MRI data, we employed open-access resources from BraTS 2020 and clinical image datasets. We also used the U-Net, 3D-Unet, D-UNet, and nnU-Net as our backbone models for tumor segmentation. Experimental results validated that the training and inference time using the proposed g-fDDFT significantly reduced by 57% and 52%, respectively, compared to that using the naïve D-UNet. Meanwhile, the accuracy estimations of brain tumor image segmentation had comparable results between the backbone models and utilizing the g-fDDFT. The geometric multi-view classifier also benefited the recognition and segmentation of tumor and necrotic/peritumoral edema images of the clinical dataset. The capability of selecting tumor candidates and fast labeling also exhibits the exclusive performance of g-fDDFT. Its flexibility and corresponding physical constraints also reveal the possibility of model extension.
Open-Circuit Fault Diagnosis Method of ANPC Three-Level Inverter Based on Joint Optimized Echo State Network

A method based on Joint Optimized Echo State Network for inverter diagnosis is proposed. Firstly, the characteristics of the leg voltages under different fault conditions are extracted by wavelet packet analysis, and then low dimensional and discriminating feature vectors are extracted by Linear Discriminant Analysis. Next, Echo State Network optimized by an improved PSO is adopted to classify feature vectors of the wavelet packet energy spectrum. Simulation results prove the proposed method in this paper has more accuracy and faster detection speed than other models in the comparison experiment.
Analysis of Focused Web Crawlers: A Comparative Study

This research paper presents a comparative study of focused web crawlers, specialized tools designed for targeted information retrieval. By conducting a systematic analysis, the study evaluates the performance and effectiveness of different crawlers. The research methodology involves selecting crawlers based on specific criteria and employing evaluation metrics. Multiple datasets are utilized for experimentation. The comparative analysis provides insights into the strengths and weaknesses of each crawler, aiding in understanding their efficacy in information retrieval. The paper discusses the implications of the findings, addresses limitations, and offers recommendations for future research. Overall, this study contributes to the advancement of focused web crawling techniques, benefiting researchers and practitioners in the field.
An Efficient Implementation of Fuzzy Classifications on Object-Oriented Database Over Relational Database

In this work, we use fuzzy logic to both the social data set and the object-oriented database, and the results are substantially more promising when applied to the object-situated data set. A Relational Data Base Management System (RDBMS) is a product framework that is used to characterize, store, and retrieve specific data from a data set. Clients must occasionally use ambiguous, confusing, or fuzzy language in their inquiry. Our work enables clients to query the database in plain English using FRDB, allowing them to provide a range of results to the client's table. It enables them to present a wide range of alternatives to the client's table at each half-variety. The name “fuzzy information base” refers to its hypothetical formalization's use of fuzzy logic and the range of attributes ranging from 0 to 1. Because of the high level of uncertainty and vulnerabilities in clinical medical services information, a fuzzy article located information base is intended to reduce fuzziness in the fuzzy social data set. Certain fuzzy inquiries are done on both the fuzzy social data set and the fuzzy item located information base in this section to validate the presentation and sufficiency of the fuzzy logic on both data sets.
Orphan Entity Allocation Using Entity Chunking, Context Mining, and Semantic Networks for CV-Parsing

The influx of job applications for a particular job poses a significant challenge for recruiters using traditional means in hiring candidates in an acceptable time frame. Employing artificial intelligence and specifically Natural Language Processing for this task presents a novel approach with expansive potential. In large part, the capability of a computer to learn and identify CV terms poses the biggest bottleneck in its flawless, commensurate execution in comparison to its human counterparts. This research presents advancements in the allocation of orphan terms in CVs specifically using Context Mining, and Entity Chunking, supplemented with Semantic Network construction. This research's potency is highlighted through extensive experimentation and evaluation in identifying orphan entities. The expansive and flexible characteristics of the Semantic Network in providing valuable insights through information representation and categorization are paramount to our research.
Real Time Hand Sign Language Recognition Using Deep Learning: A Robust and Efficient Model for Improved Communication Accessibility

People with special needs shouldn't be left behind in a world where technology is developing quickly. Deaf people may communicate themselves beautifully using sign language, but sadly, it can be challenging for others to comprehend. Our state-of-the-art computer vision system enters the picture here. We have developed a technology that can properly interpret hand movements in real time using neural networks and sophisticated machine learning algorithms. Our system achieves astounding accuracy rates of 90% by using the most recent development libraries, including Tensorflow, Python, and OpenCV. With the help of this ground-breaking technology, we are removing obstacles to communication and enhancing accessibility for everyone. We are trying to emphasize on the impact of the technology on accessibility and inclusivity for all individuals, including those with special needs. The system's ability to interpret sign language in real-time can remove communication barriers and enable deaf individuals to communicate effectively with others.
Adversarial Attacks against Traffic Sign Detection for Autonomous Driving

Deep neural networks play a crucial role in 2D object detection based on visual data, but they are also vulnerable to adversarial samples. Attackers manipulate low-resolution images to execute data poisoning attacks. This paper introduces a method to generate realistic high-resolution adversarial samples aimed at compromising traffic sign detection models. Specifically, we propose a high-resolution adversarial sample framework built upon generative adversarial networks. Subsequently, an adversarial traffic sign detection model is developed to investigate the impact of data poisoning. To enhance the model’s robustness, we conduct adversarial training. Experimental results demonstrate the efficacy of our data poisoning approach in misleading the detection model. Furthermore, the detection model exhibits improved robustness against such attacks following adversarial training.
Migration Techniques for Virtual Machine Distribution Using Machine Learning Heuristics Application in Cloud Computing

Cloud computing has revolutionized the way IT resources are provisioned, deployed, and managed. Virtual machine (VM) migration is a crucial aspect of cloud computing that facilitates resource optimization, load balancing, and fault tolerance. In recent years, machine learning (ML) heuristics have emerged as effective tools for VM distribution, allowing dynamic and intelligent allocation of resources. This research review paper presents a comprehensive analysis of various migration techniques that leverage ML heuristics for VM distribution in cloud computing environments. The paper reviews relevant literature, compares different methodologies, and discusses their advantages, limitations, and potential applications.
‘’Impact of Visual Encoding on Automation Transparency in Drone Interface

With the civilianization of consumer drones, the market of drone apps is expanding. As a key medium of human-machine interaction, the well-designed interface can reduce accident rate and boost mission success rate. Automation transparency is a descriptive metric of interface usability in unmanned systems, reflecting the interface’s ability to improve users’ situation awareness. This study investigated the impact of various levels of visual encoding on automation transparency. First, three typical flight modalities were selected as test scenarios, including obstacle avoidance, dynamic tracking, and waypoint flight. Then, each of the three modes were all visually encoded at three levels by introducing more visual channels. A single-factor independent experiment was conducted, in which participants completed the corresponding tasks. The experiment results showed that the level of visual encoding had a significant impact on interface automation transparency. Participants achieved better performance in most of the test tasks as the level of visual encoding rose. However, the upward trend from level 2 to 3 tended to level off. Meanwhile, the cognitive load of the participants showed a slight rebound following a decline. This research also revealed disparities in the efficacy of the several visual channels, with the position being more potent than color, texture, and shape.
Real-Time Human Pose Estimation: A MediaPipe and Python Approach for 3D Detection and Classification

As the name suggests, this study uses the Open- Pose and MediaPipe frameworks in order to give a thorough analysis of real time human posture detection and identification. Predicting the locations of key body joints in a person's stance is a typical computer vision issue known as human posture identification.” We use the strength of OpenPose and MediaPipe to quickly and reliably identify different positions. Our approach concentrates on particular stances, such as the T-pose, Tree-pose, and Warrior pose, and gives users visual feedback while watching a live camera feed. The system analyses and recognizes postures using a structured output of body key points, confidence ratings, and pixel coordinates. The project runs well under ideal lighting settings and with adequate hardware resources, while it may occasionally lag and show inconsistent behavior under difficult circumstances. Through algorithm optimization and hardware upgrades, future work can overcome these restrictions and improve the system's performance. With potential applications in fitness tracking, virtual reality, and rehabilitation, this study advances the field of human posture detection and recognition.
A Command-Line Interface Tool for Efficient Git Workflow: “Commandeer”

Commandeer is a command-line interface (CLI) tool designed to enhance the git workflow by seamlessly integrating with Visual Studio Code (VS Code). It provides an efficient and suitable way to open files directly from the terminal, streamlining the process of navigating and editing code within a git repository. This CLI tool offers a set of in-built commands that enable common git operations, such as opening files associated with a specific commit or staging changes for review. With commandeer, developers can harness the power of VS Code while staying focused on their terminal-based workflows. By reducing context switching and enhancing productivity, commandeer aims to improve the development experience and empower developers to be more efficient and effective in their day-to-day coding tasks. It provides tangible benefits that enhance productivity, collaboration, and efficiency in software development. This research paper proposed commandeer a CLI tool and its features, functionality, and evaluates its impact on the git workflow through a real-world use cases and performance measurements.
Unveiling Emotions in the Digital Arena: Sentiment Analysis of YouTube Comments on FIFA World Cup 2022

YouTube is a popular video sharing website and a social networking platform where users can upload (only registered users), watch and share videos on any particular topic with anyone who can access the site and also share their views about the video in the comment section. Since the YouTube comment section has no word/character limit, the users/viewers can easily express their thoughts and viewpoint in great detail. Also, in the comment section, the users make use of all kinds of emoticons, abbreviations, images, GIFs, etc. In this paper, we deploy different machine learning algorithm along with the Tf-IDF (term frequency- inverse document frequency) method on the comments collected from various YouTube videos on the FIFA World Cup 2022 and compute an accuracy of 81.81% with Random Foresta and 83.03% with Logistic Regression
Evaluation of Focused Web Crawlers in Real-World Scenarios

This study evaluates the practical performance of focused web crawlers, tools essential for extracting targeted data from the ever-expanding web. Moving beyond theoretical analysis, we configure crawlers for specific real-world scenarios such as academic research and social media monitoring, optimizing them for precise data retrieval across a dataset of 1,000 domains and 10 million URLs. Performance is measured through meticulous logging of resource use and data freshness, while quality is ensured via cross-verification with a benchmark dataset. Ethical compliance is rigorously maintained throughout the process. The comparative analysis of different crawlers provides a framework for their selection and deployment, culminating in actionable recommendations for their use in various industries, reflective of the complex demands faced by users in a dynamic digital landscape.
The New IoT Driven World: Overview and Perspectives

In this note we consider the impact of the IoT on our society. In more detail we will consider the digital transformation that is radically changing society, the problem of the large mass of raw data that must be transformed to become usable and intelligible and the question of the energy transition which cannot occur except through the decentralization of production of energy.
Analysis for Text Detection on Scenic Images Using Deep Learning

Text detection in scenic images is the process of precisely localizing instances of text in them. This detection is required for a numerous computer vision applications including text recognition, autonomous vehicles, script narrators, translation apps etc. Text detection and localization refer to the process of identifying and locating text within an image or document. Scene text detection was mostly limited to putting together handcrafted low-level attributes of the texts contained in images until the emergence of deep learning in this area. A combination of methods or exploring domainspecific approaches help in enhancing the efficacy of detecting text in scenic images. In this paper, we have implemented the performance of three text detection models EAST, CRAFT and Text Fuse Net on two benchmark public datasets used for detection and localization. Experiments on two benchmark datasets show that the Text Fuse Net achieves state-of-the-art performance. Specifically, we have achieved an F measure of 94.6% and 92.2% on IC-2013 and IC-2015. respectively, followed by CRAFT [89.8%, 84.8%] and EAST [85.3%, 80.7%1.
Semantic Segmentation Research of Motion Blurred Images by Event Camera

The semantic segmentation of road objects is a prerequisite for autonomous driving. Rapid or bumpy movement of vehicles can lead to the images to be blurred, which reduces the safety of autonomous driving. This study combines the dynamic visual sensor (DVS) and RGB camera to improve the detection accuracy of road semantic segmentation. Firstly, based on DVS and RGB images, we develop two datasets of blurred images, including the stone and run datasets. Then, a double channels’ detection network is designed with several Swin-transformer modules, and the two channels are designed to extract features from DVS and RGB images, respectively. In addition, a feature fusion module is designed to extract attention from DVS images. Finally, experiments demonstrate that our proposed double-channels’ fusion method achieves the state-of-the-art segmentation performance for blurred images.
Deep Recurrent Speeded Robust Feature Learning Based Bagging Ensemble Multinomial Regressive Cancer Classification Using Mammograms

Breast cancer is one of harmful diseases which impact the living rate of women life. There is currently no proficient way to protect the women life from breast cancer. However prediction of cancer disease in previous stages can raise the possibilities of patient's being recovered and surviving. In existing, various Recurrent Neural Network (RNN) models were designed to perform breast cancer analysis. But, feature extraction performance of conventional RNN is reduced by gradient issues which increase training time, poor cancer classification performance, and also does not give better accuracy for early identification. Therefore, a novel Deep Recurrent Speeded Robust Feature Learning Based Bagging Ensemble Multinomial Regressive Classification (DRSRFL-BEMRC) Model is intended in this paper. The DRSRFL-BEMRC Model initially implements Deep Recurrent Speeded Robust Multiple Feature Extraction (DRSRMFE) algorithm with the goal of extracting relevant features in mammograms with lesser time consumption through operating well to rotation-invariant, robust to noise and computation time and accuracy. Subsequently, Bagging Ensemble Multinomial Regressive Cancer Classifier (BEMRCC) algorithm is constructed to exactly classify an input mammograms as normal, benign or malignant based on the discovered features and thereby lessens false rate of cancer analysis for early recognition. The efficiency of proposed CWDCMFE-MBRC technique is analyzed by taking the factors such as disease classification accuracy, disease classification time, and sensitivity, specificity and false rate depends on dissimilar numbers of input mammograms.
Identifying Abnormal Energy Consumption Patterns in Industrial Settings: Application of Local Outlier Factor Algorithm for a Processing Factory in Vietnam

In practice, the energy consumption of industrial equipment rises mostly due to wear and tear, which might include leaks or faulty plant conditions. By using statistical techniques and artificial intelligence, energy managers can promptly identify abnormal energy usage and determine the underlying causes for such anomalies. There are various machine learning algorithms powerful enough to become anomaly detectors; however, relevant candidates must be sufficiently accurate and rapid response for industrial application. Thanks to suitable performance on different types of unsupervised anomaly detection, the Local Outlier Factor approach has been selected. The aim of this study is to identify abnormal energy consumption patterns for preventing energy loss and promoting predictive maintenance in a beverage processing factory in Vietnam. The anomaly score has been evaluated to detect multivariate and subsequence anomalies. Finally, energy consumption time-series with anomalous points highlighted has been provided for the business user to be actionable on the anomaly.
Technical Research on Computer Network Security Automatic Evaluation

The rapid development of information technology has greatly improved the level of enterprise informatization. However, while providing a variety of services to users, there may also be security issues such as data breaches, denial of service attacks, and extortion attempts. To address the aforementioned issues and enhance the security of information systems, multiple security devices have implemented protective measures. However, relying solely on the configuration and construction of security facilities cannot fundamentally resolve network security issues. Therefore, an important challenge currently faced is how to quantitatively assess, evaluate, and guide security personnel to optimize the configuration of the network security system. This is a significant matter that requires careful consideration.Currently, most existing quantitative security evaluation schemes focus on assessing security risks, but the entire security protection system has not been utilized as an evaluation criterion, and its protective effectiveness has not been quantitatively evaluated. Simultaneously, establishing quantitative connections between security impact factors and the security protection system, linking the protection effectiveness of individual security devices to overall security protection effectiveness, and then optimizing and improving the protection effectiveness have become critical bottlenecks in measuring the protection effectiveness of the network security protection system.