Reviewerly/exHarmony huggingface dataset:
Resilience Cloud Security Framework

Cloud security refers to the practices, technologies, policies, and measures designed to protect data, applications, and infrastructure in cloud computing environments. Cloud security is crucial because organizations are cloud for their data storage which is very sensitive in nature and process critical applications in the cloud, making them potential targets for cyberattacks. Because of the ever-increasing popularity, cloud computing has garnered considerable attention during the last several decades. Businesses are getting benefited by using Cloud data storage like virtual use of Information Technology infrastructure and their management, virtually remote access from any location with a steady connection to the internet. The cloud storageis also providing monitoring savings. Anyone can easily access the cloud storage on pay-as-you-go model which needs more investigation towards the security and privacy of cloud computing. The paper includes the criticalliterature of Resilience Cloud Security Framework which acts as an Antivirus for securing cloud Systems against Cyber Attacks through Cryptography Techniques. The paper covers cloud security concerns and requirements along with acknowledged cloud attacks and vulnerabilities. The purpose of the paper is to analyze variouscloud security components along with privacy issues. The paper includes a new kinds of security solutions. The paper is survey based and focused on different kinds of security vulnerabilities that risked the services of cloud. The paper proposed future possibilities and explores in depth the security concerns that cloud companies such as cloud vendors, data owners, and cloud users face.
Detection of Breast Cancer from Mammogram using Convolution Neural Network

Abstract: Breast cancer is the leading cause of cancer among women. In the last few years, the number of cases of breast cancer has skyrocketed among younger women. Detection of breast cancer during the initial stages can greatly reduce the risk of fatality. Mammograms which are X-ray images of breast tissues are used extensively by doctors to determine the early onset of breast cancer. However, due to human error, a lack of resources and knowledge can result in inaccurate predictions which can prove fatal. The power of Artificial Intelligence to process and predict images has greatly increased in the past few years. Many modern medical devices utilize the power of sophisticated AI algorithms to aid in detecting and predicting the early onset of breast cancer. In this paper, we demonstrate how we can utilize a convolution neural network to predict the early onset of breast cancer and help solve the issue of inaccurate detection of cancer cells from mammogram images.
Artitude App

Abstract: As everyone is aware, there is a great demand now for an acceptable platform where people can share their thoughts and tactics with the globe.In addition to academics, art is quite significant. Therefore, this is our app, which offers a platform to display any kind of art classes and extracurricular activities. which encourages people to take part in various competitions by assisting them in developing their extracurricular skills. Thus, with the aid of this app, a direct connection between the teacher and the student will be established, making the working processes simpler and more practical. Additionally, it aids kids in receiving the proper direction from the appropriate source.
Break the Bias: Delving Semantic Transform Invariance for Few-Shot Segmentation

Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images. Existing FSS algorithms typically focus on mining category representations from the single-view support to match semantic objects of the single-view query. However, the limited annotated samples render the single-view matching struggle to perceive the varying characteristics of novel objects, which results in a restricted learning space for novel categories and further induces a biased segmentation with demoted parsing performance. To address this challenge, inspired by the semantic transform invariance, this paper proposes a fresh few-shot segmentation framework to break the bias and perform invariant segmentation in a multi-view matching manner. Specifically, original and transform support features from different perspectives with the same semantics are learnable fused to obtain the transform invariance prototype with a stronger category representation ability. Simultaneously, aiming at providing better parsing guidance, the Transform Invariance Guidance Mask Generation (TIGM) module is proposed to integrate prior knowledge from different perspectives. Finally, segmentation predictions from varying views are complementarily merged in the Transform Invariance Semantic Prediction (TISP) module to decide the uncertain area and yield precise segmentation predictions. Extensive experiments on both PASCAL-5i and COCO-20i datasets demonstrate the effectiveness of our approach and show that our method could achieve state-of-the-art performance. Code is available at https://github.com/caoql98/BBD.
Shrink: Identification of Encrypted Video Traffic Based on QUIC

With the increasing prevalence of network videos, video traffic has become a significant portion of overall network traffic. Due to the presence of harmful content such as pornography and violence in network videos, network monitoring is necessary. However, the encryption of videos poses challenges for network monitoring. More and more video service providers are adopting QUIC as the default video transmission protocol to accelerate data transfer speeds. However, the existing methods for identifying encrypted video traffic do not apply to QUIC. Video service providers typically employ Content Delivery Network (CDN) technology to enhance user experience, which can result in missing video chunks for side-channel identification. Additionally, fluctuations in network conditions can lead to the retransmission of video chunks. This paper proposes Shrink, a QUIC-based encrypted video traffic identification method. It effectively extracts video chunks from online QUIC encrypted video traffic and proposes a bucket structure and global-local match to alleviate the issues of video chunks retransmission and loss. Furthermore, a bucket word dictionary is designed to enhance the method’s running speed. Experimental results demonstrate that Shrink performs well in real network environments, exhibiting superior accuracy and speed compared to existing state-of-the-art methods.
Obstacles to Digital Transformation of Labor Education in Chinese Universities, Logical Rationale and Implementation Strategies

The new development stage of labor education in Chinese colleges and universities focuses on "transformation", and the external obstacles arising from it cannot be ignored. First, the traditional form of labor education in Chinese colleges and universities conflicts with the integration of digital disciplines and professions; second, the goal of traditional labor education in China is misaligned with the digital labor demand mosaic; third, the main body of traditional labor education in China is divergent from the main body of the digital technology class. As a result, the analysis of the basic logical reasoning of the digital transformation of labor education in Chinese colleges and universities: first of all, we should follow the Marxist view of labor, secondly, based on the basic concept of labor education, and finally, balance between physical labor and digital labor. In order to solve the obstacles to the digital transformation of labor education and comply with the basic logic, it is necessary to adopt the strategies of consensus and common research to build lifelong labor education, construct a three-dimensional form of technological innovation and labor education, and open up practice bases to promote the value of digital labor, in order to promote the digital transformation of labor education in China's colleges and universities to the depth.
Large period spiking andburstingin anexcitable system with memory

Excitability in dynamical systems refers to the ability to transition from a resting stationary state to a spiking state when a parameter is varied. It is the mechanism behind spike generation in neurons. Optical non-linear resonators can be excitable systems, but they usually present a fast response compared to neuronal systems, and they prove difficult to observe experimentally. We propose investigating optical resonators with delayed Kerr effects, specifically in two different geometries: an oil-filled single-mode cavity with thermo-optical nonlinearity, and two coupled, symmetrically driven cavities. When the Kerr effect is delayed, even a single cavity exhibits excitability. However, we show that it suffers from limitations on the thermo-optical relaxation time in order to be realized experimentally. We overcome these limitations using the geometry with coupled cavities, where the thermo-optical relaxation time acts as a memory. This slow variable enables to tailor the spiking frequency and it mimics neuronal behaviours by enabling large-period spiking.
School Violence Detection with CNN and LSTM

In Vietnam, the problem of school violence is now a growing concern. Accompanying the technology development, Artificial Intelligence is a good practice for detecting school violence. This paper proposes the work to examine whether ResNet-50 performs better than VGG19 in school violence detection. Moreover, this paper also presented an easy-to-manage school violence detection web application from real-time captures of CCTV connected to the hosting server. The research picked the transfer learning method for Keras’ pre-trained CNN (Convolutional Neural Network) models (ResNet-50 and VGG19) with LSTM (Long Short-Term Memory). Python was used to train and build the models and run the detection program. The web application was coded in JavaScript, HTML, and CSS. We used the model with the best performance and built the web application with it. Results suggested that the VGG19+ LSTM model had higher accuracy than the ResNet-50+ LSTM model in school violence detection. Finally, the web application had a friendly GUI and returned real-time detection results.
Learning Curves Prediction for a Transformers-Based Model

One of the main challenges when training or fine-tuning a machine learning model concerns the number of observations necessary to achieve satisfactory performance. While, in general, more training observations result in a better-performing model, collecting more data can be time-consuming, expensive, or even impossible. For this reason, investigating the relationship between the dataset's size and the performance of a machine learning model is fundamental to deciding, with a certain likelihood, the minimum number of observations that are necessary to ensure a satisfactory-performing model is obtained as a result of the training process. The learning curve represents the relationship between the dataset’s size and the performance of the model and is especially useful when choosing a model for a specific task or planning the annotation work of a dataset. Thus, the purpose of this paper is to find the functions that best fit the learning curves of a Transformers-based model (LayoutLM) when fine-tuned to extract information from invoices. Two new datasets of invoices are made available for such a task. Combined with a third dataset already available online, 22 sub-datasets are defined, and their learning curves are plotted based on cross-validation results. The functions are fit using a non-linear least squares technique. The results show that both a bi-asymptotic and a Morgan-Mercer-Flodin function fit the learning curves extremely well. Also, an empirical relation is presented to predict the learning curve from a single parameter that may be easily obtained in the early stage of the annotation process. Doi: 10.28991/ESJ-2023-07-05-03 Full Text: PDF
Dotriacontal Number System in Computer and Error Detection

n this this paper for the first time, we have formulated the dotriacontal number system using existing number systems like binary, and hexadecimal numbers. The dotriacontal number is one with a base of 32, containing 32 single-character digits or symbols. Each symbol contains five binary digits. This number system can be used in computers for reducing memory consumption and for memory specification. Generally, the computer memory addresses are displayed in five hexadecimal integers. If the computer memory addresses can be showed in four dotriacontal integers rather of five hexadecimal integers, the memory consumption will be reduced. It also can be used to increase the number of MAC addresses, IPv4 and IPv6 addresses. MAC (Media Access Control) address can be constructed as six deckles or 12 integers of the dotriacontal numbers instead of six octet hexadecimal digits. The IPv4 address can be defined in 8 integers of dotriacontal number that is 240 that is 1099511627776 address, and also the IPv6 address can be designed in 32 digits of the dotriacontal number that is 2160 or 1.46150164E48 addresses. The dotriacontal number system can be effective to detect error message using the checksum method. Typically, the checksum method is used by a binary or hexadecimal number system but the checksum method can be easily applied in the dotriacontal number system to detect error messages. The proposed work of this paper will be implemented if the dotriacontal code is executed and the memory specification is defined as a 10 bits deckle system instead of 8 bits byte.
Reducing Cloud Expenditures and Carbon Emissions via Virtual Machine Migration and Downsizing

Cloud computing has grown at an unprecedented rate in recent years, which leads to two new significant challenges. First, cloud expenditure is increasing rapidly and reducing cloud expenditure has become the top priority of enterprises relying on cloud services. Second, cloud computing consumes significant amounts of energy and generates massive carbon emissions. This paper presents a comprehensive study to reduce the cost and carbon emissions of cloud computing. Through analyzing over 2.6 million real-world Azure virtual machines (VMs) and carbon intensity data provided by WattTime, it proposes a metric to quantify cloud waste and trains a machine learning model to estimate the power consumption and carbon emissions of VMs. Furthermore, it proposes two VM scheduling algorithms, Reschedule by Threshold (RT) and Reschedule by Averages (RA), to reduce the carbon emissions of cloud workloads, and the other two algorithms, the Shutdown (SD) algorithm and the Core Reduction (CR) algorithm, to reduce both cloud cost and carbon emissions. The simulation results running on the 2019 Azure VM trace demonstrate that the proposed algorithms could reduce nearly 3.5 million pounds of CO2 emissions and help cloud users save approximately ${\$}$13.8 million dollars per month.
Prediction of thickness of blasting scatter water belt based on GA-BP and PSO-BP

In this paper, a method for predictive analysis is proposed based on BP neural network optimized by genetic algorithm and BP neural network optimized based on particle swarm algorithm. By optimizing the initial weight and threshold of the neural network by the algorithm, showing the error between the predicted value and the real value, and comparing the mean squared error of the three, the MSE of the GA-BP prediction model is reduced by 56.53% compared with the BP neural network prediction model. The MSE of the PSO-BP network predictive model was reduced by 50.00%. The results show that this method can accurately predict the thickness of the blasting protective water belt, which provides a new and reliable method for the study of controlling blasting flying objects.
SOSIALISASI PERKEMBANGAN TEKNOLOGI PERTANIAN DI ERA REVOLUSI INDUSTRI 4.0 DI SMP NEGERI 10 PAGARALAM

Pagar Alam City is a fairly large producer of agricultural commodities in South Sumatra Province. As producers of vegetables and coffee, many farmers have low education and do not know much about developments in the world of agriculture. The industrial revolution era 1.0 was marked by the presence of mechanical processes using machines. This era is also marked by the energy generated by steam and water. Daily activities that were originally carried out by humans and animals are increasingly being replaced by machines. This service activity can provide benefits for students of SMP Negeri 10 Pagar Alam to increase their knowledge about the industrial revolution 4.0 and introduce technologies used in agriculture and processing of agricultural products.
OCR Pipeline for Transforming Parliamentary Debates into Linked Data: Case ParliamentSampo - Parliament of Finland on the Semantic Web

&#x0D; &#x0D; &#x0D; This paper presents the OCR pipeline created for ParliamentSampo - Parliament of Finland on the Semantic Web, a Linked Open Data (LOD) service, data infrastructure, and semantic portal for studying Finnish political culture, language, and networks of the Members of Parliament (MP). A knowledge graph of linked data has been created based on ca. 967 000 speeches in all plenary sessions of the Parliament of Finland in 1907—2022; the data is also available in XML format, utilizing the new international Parla- CLARIN format. A central part of the historical debates 1907-1999 was available only as PDF documents of fairly low OCR quality and had to be OCRed first; this paper reports lessons learned from this process.&#x0D; &#x0D; &#x0D;
Transliteration Model for Egyptian Words

In this paper, we describe token-based transliteration models for Egyptian words. We explain how we created them using an automatic alignment method we devised based on the Needleman-Wunsch sequence alignment algorithm. We use two sources where encoded Egyptian hieroglyphs and their transliteration pairs are available. Ancient Egyptian Sentences (AES) includes a collection of texts where c. 254,000 Egyptian words encoded using Manual de Codage (MdC) have been aligned with their transliteration counterparts. The second source is the Ramses Transliteration Corpus (RTC), with almost 500,000 MdC encoded words. The RTC consists of encoded hieroglyphic sentences, each on its line, and respective transliteration lines in another file. However, unlike the AES, there is no ready alignment of the MdC and its transliteration on the word level. In order to find word-transliteration pairs, we align the sentences of encoded words with the respective transliterations. The alignment task is made more difficult because many of the texts contain damaged parts and editorial additions.
Human Gesture Recognition for Elderly People Using User Training Interaction Data

Research on human-computer interaction (HCI) has been widely developed for older people. However, there needs to be more research studies on the deep learning model implementation of human gesture image data to monitor the activities of older people. There are four main stages of research, including data preparation, feature extraction using pre-trained models VGG16 and VGG19, training without and with fine-tuning, and comparing the performance of the deep learning model. This study used the dataset of Ralf Leistad Gesture with data classes as backward, forward, left, right, still, and stop. Then, the data is implemented in the data augmentation method using rotation, brightness, width shift, height shift, horizontal flip, and vertical flip. As a result of the experiment, VGG16 achieved an accuracy of 96.88%, and VGG19 reached an accuracy of 96.88%.
The Pecos Array: A Temporary Nodal Seismic Experiment in the Pecos, Texas, Region of the Delaware Basin

Abstract With increasing earthquakes in the Delaware basin since 2009, earthquake studies, including accurate hypocenters, are critically needed in the Delaware basin to identify the structures producing earthquakes, and to determine if they are related to unconventional petroleum development and production. In 2018, with funding from the Texas Seismological Network, we deployed and maintained a nodal network of 25 Magseis Fairfield Z-Land Generation 2 5-Hz seismic nodes in the Pecos, Texas, region of the Delaware basin, known as, The Pecos Array. The network was deployed from November 2018 to the beginning of January 2020, with an additional two months of data recorded in September and October 2020. The network collected continuous three-component data with a 1000-Hz sampling rate. The spacing of the nodes varied from ∼2 km in town to ∼10 km farther away from the city center. The primary goal of this network was to improve estimation of event hypocenters, which will help to determine why there has been an increase in earthquakes over the past several years. In this article, we summarize the scientific motivation, deployment details, and data quality of this network. Data quality statistics show that we successfully collected continuous data with signal-to-noise ratios that allow us to detect and locate events, hundreds of them being estimated at ML&amp;lt;0.50. This unique dataset is contributing to new seismotectonic studies in the Delaware basin.
Accurate UAV-Based Vehicle Detection: The Cutting-Edge YOLOv7 Approach

The progress in unmanned aerial vehicle (UAV) technology has brought about a transformative impact on aerial surveillance and monitoring applications, with a particular focus on vehicle detection in fields such as traffic management, urban planning, and security. Despite these advancements, conventional vehicle detection methods encounter challenges when dealing with complex scenarios, real-time processing, and scalability. To address these issues, this research proposes an advanced AI-driven vehicle detection system that utilizes the state-of-the-art YOLOv7 model. The YOLOv7 model, known for its cutting-edge object detection algorithm, excels in real-time processing and precise object localization in both images and video frames. In this study, we leverage the capabilities of YOLOv7 for detecting vehicles from UAV imagery. By integrating the YOLOv7 model with UAV data, the proposed system achieves robust and efficient vehicle detection across diverse environmental conditions. To evaluate the performance of our proposed system, extensive experiments are conducted using a real-world UAV imagery dataset. These evaluations involve comparing the AI-driven vehicle detection system with existing methods in terms of accuracy, processing speed, and scalability. The results clearly demonstrate the superior performance of the YOLOv7-based system, achieving high detection rates with minimal computational overhead. This research significantly contributes to the field of vehicle detection from UAV imagery by introducing an advanced AI-driven approach that utilizes the YOLOv7 model. The proposed system has the potential to enhance various applications, including traffic monitoring, accident prevention, and urban planning. Additionally, this study offers valuable insights into the integration of deep learning algorithms with UAV technology, paving the way for future advancements in aerial surveillance and monitoring systems.