Reviewerly/exHarmony huggingface dataset:
Auto Scaling Infrastructure with Monitoring Tools using Linux Server on Cloud

Cloud computing is the term that has gained widespread usage over these last few years. Due to the rapid increase in the use of information in the digital age of the 21st century, it is increasingly becoming a more attractive option for individuals and organizations to manage all their essential data, projects, and collaborations, rather than relying solely on in-house computers. The user's requirement for hardware and software is reduced via cloud computing. The interface software of cloud computing systems, typically as simple as a web browser, is the only thing the user must operate, and the Cloud network handles the rest. To decrease operational costs, both business and government organizations are adopting cloud computing, seeking a flexible and adaptable solution for the supply and delivery of their product services. Microservices and decoupled apps are becoming more popular. These container-based architectures make it easier to build sophisticated SaaS apps quickly, but managing and creating microservices can be a daunting task. Managing and creating microservices that involve a wide range of diverse functions, including handling and storing information, and performing predictive and prescriptive analysis, can be a challenging undertaking. Establishing auto scaling infrastructure on doud can be challenging due to several reasons, some of which are: understanding the application architecture, setting up monitoring, scaling policies, cost optimization and implementation complexity. Server farms include the tremendous and heterogeneous virtualized frameworks, which are continually extending and broadening after sometime are the essential starting point for registering specialized organizations. These solutions also need to be integrated into existing systems while adhering to Quality of Service (QoS) requirements. The principal objective of this work is to propose an on-premise design to leverage Kubernetes and Docker containers to improve the quality of service based on resource usage and Service Level Objectives (SLOs). The Prometheus Administrator set up is used to perform namespace checking. Normally, doud providers enable their own monitoring tools (like CloudWatch) for monitoring CPU, storage and network usage, service component, however these tools cannot monitor the service component. Additionally, the advancements have restricted the capacity to follow QoS highlights at the application level (like security and execution) since the main focus will be dedicated towards the equipment assets. These types of node-level monitoring make it difficult to scale requests and deploy pods to match the demand. Infrastructure monitoring should enable runtime changes to monitor the requirements or metric operationalization should be done on those criteria without modifying the underlying infrastructure.
Sentiment Analysis on Amazon Product Reviews using LSTM and Naive Bayes

The majority of consumers who prefer to purchase goods online from e-commerce websites frequently rely on reviews that have previously been written by other consumers or a summary of those reviews. For manufacturers and enterprises, opinion data is crucial. To systematically look through every post on the internet and glean insightful viewpoint information from it would be unrealistic. There is too much data to handle if you do it manually. Sentiment analysis is particularly utilized to find favorable, unfavorable, or inconsistent features of products in online text evaluations. The previous studies have given reviews only for a particular category of products. But the current technologies have failed to give the overall review of each product. This approach is used to provide the ultimate review of each online product, whether it is good, negative, or neutral, to overcome this drawback. To conduct the study between two different supervised learning techniques, Long short-term memory and Naive Bayes, have been attempted on online Amazon products. Their accuracies have then be compared.
Streamlining Medicine Delivery through Automation

The average person searches for an effective and reliable online pharmaceutical delivery system because they spend a lot of money and time on medications, which is unusual. Most individuals try to pick up their medications from their neighborhood pharmacies to avoid sitting in traffic, but occasionally they are sent away empty-handed because the medications they require are out of stock. A web-based e-commerce system for the distribution of pharmaceuticals is essential during periods like this and at night when pharmacies are closed. The proposed method uses Global Positioning System (GPS) to find the closest pharmacy and to speed up deliveries and online transactions to decrease these problems. Additionally, it offers helpful details about treatments and illnesses, as well as references to important medical websites, organizations, and government agencies. Buying a variety of drugs is made simple by online pharmacies. These offer several advantages, including reduced cost, convenience of accessibility, doorstep delivery, and so forth. Despite all the difficulties, a person can utilize the internet to obtain prescription medications from licensed pharmacists and have them delivered to their doorway or the closest delivery location without leaving the comfort of their home. Traditional pharmacies are starting to be replaced by the e-pharmacy concept, which is exceedingly easy to use and is quickly spreading. Existing techniques used HTML, CSS, JAVASCRIPT, VEUJS, EXPRESS FRAMEWORKS, AJAX for frontend and backend, on the other hand, the PHP, MYSQL, HTML, CSS and existing techniques are not providing the expiry date of the concerned medicine. This leads to the drug poison, whereas in the proposed model, the medicine is deleted from the user module, when it exceeds the expiry date or the medicine is out of stock.
Pancreatic Cancer Classification using Deep Learning

The great majority of the computer systems that are now being utilized for research on medical health systems are based on the most recent technical breakthroughs. Because of the prevalence of pancreatic cancer, a significant number of novel approaches and techniques have emerged in the field of medicine. There are several various classifications that may be applied to the pancreatic cancer that can be found. Utilization of the deep learning technology is going to be the means by which the classification of pancreatic cancer is going to be completed. The classification of pancreatic cancer may be tackled from a variety of angles, each of which can be accomplished via using either technology for machine learning or technology for deep learning. In the past, a diagnosis of pancreatic cancer could be made by using methods such as the Support Vector Machine (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines. However, these methods are no longer effective (TWSVM). However, these strategies do not deliver an accurate performance. As a result, this study has implemented an Advanced Convolution Neural Networks (ACNN), which are examples of the type of technology known as deep learning. In the vast majority of the existing research works, the classification has been determined by analyzing the images of the patient, which are not always accurately classified; in contrast, the classification in this one is determined by looking at the genetic data of the patient. An accurate number can be obtained by using the blood and urine samples collected from patients since these samples were utilized to construct the genetic data. With the help of constant values and ACNN strategies, the performance rate was enhanced in contrast to the approaches that were currently being used.
American Sign language Recognition using Deep Learning

Communication plays a vital role in day-to-day life but consider a scenario in which two people are unable to communicate with one another because one of them does not comprehend what the other person is attempting to say. Most of the deaf-mute community encounter this when conversing with ordinary folks. As sign language is used by persons with impairments, normal people don'tknow or lacks understanding of it. This communication gap must be bridged. Therefore, a model has been developed to assist normal people and enable deaf-mute individuals to communicate with one another. One such model is the sign language detection system, which uses a deep learning strategy to identify American Sign Language (ASL) gestures and output the corresponding alphabet in text format. A CNN model and YOLOv5 model were built and compared against each other. YOLO model has produced an accuracy of 84.96%, whereas CNN model has produced an accuracy of 80.59%
Student Eye Gaze Tracking and Attention Analysis System using Computer Vision

Along with self-reports and periodic assessments students' engagement in online lectures can be understood with the help of eye gaze tracking. This data can provide valuable insights on the learning pattern of students and areas of difficulty which further aid the teachers in improving their teaching style. Extensive research has been performed in eye gaze tracking based on their location, shape, texture, or a combination of these features. Usually, deep learning-based neural networks are used for this purpose due to its increased efficiency and accuracy. Deep learning requires huge amount of high-quality annotated data to train the model and has high complexity. Thus, this paper proposes an approach for analyzing the attention of students in online classes by tracking the eye gaze using computer vision which is computationally cheap. This technique is being used in various applications such as human-computer interaction, cognitive psychology, and security systems. Using eye-tracking technology, researchers can follow and measure eye movements, pupil dilation, point of glance, and blinking to identify where study subjects' visual attention is focused, what they pay attention to, and what they ignore. With the help of Haar Cascade and pupil detection algorithms such as Daugman algorithm, Hough Circular Transform, Blob Detection, and Centroid Detection, the eye movements are tracked. In order to map the eye gaze onto the monitor's screen a geometric model is proposed which can classify the gaze direction into nine states with respect to the screen: center, left, right, top, top left, top right, bottom, bottom left, and bottom right. The system calculates the amount of time a student is not paying attention to the screen based on which the attentiveness in online lectures is decided. There are three classifications for students' attention states: alert, sleepy, and not attentive. This system is sensitive to glares, head movements, varying lightings, and motion blur due to the lower accuracy and difficulty in detecting the pupil by computer vision algorithms.
Twitter Sentiment Analysis using Enhanced TF-DIF Naive Bayes Classifier Approach

Public and private opinions on a wide range of topics are expressed and regularly disseminated through several social media channels. One of the social media platforms that is growing in popularity is Twitter. The emotional impact of a person plays an important role in daily life. The method of assessing a person's opinions and thought polarity is known as sentiment analysis. Here, this study addresses the sentiment classification problem on the Twitter dataset. A number of text preprocessing methods and Naive Bayes (NB) classifiers is used to perform sentiment analysis in the proposed system. Preprocessing procedures typically involve eliminating stop words, changing the case of the words to make them more normal, and using stemming or lemmatization. Twitter Sentiment Analysis is a method used for analyzing emotions from tweets. Tweets are useful in obtaining sentiment values from a user. The data provides an indication of polarity as positive, negative or unbiased values.
Hybrid Cryptography for Secure File Storage

The safeguarding of data from illegal access, use, disclosure, disruption, alteration, or destruction is referred to as data security. Data security is crucial for safeguarding the privacy, availability, and integrity of data as well as for assuring compliance with the rules and laws that control how sensitive data is handled. A potent approach for protecting digital data, including files, is hybrid cryptography. Hybrid cryptography offers quick and secure communication and storage of sensitive information by combining the benefits of both symmetric-key and public-key cryptography. A file is encrypted using a symmetric key in a hybrid cryptography system for secure file storage, and the file is then further encrypted using the recipient's public key. The recipient can then ensure that only permitted parties can access the file by decrypting the symmetric key with their private key. This method is well-liked for safeguarding sensitive data in a number of circumstances because it combines the symmetric key cryptography's effectiveness with the security of public-key cryptography. The need for storing and protecting huge volumes of data grows significantly with evolving technologies. Massive attacks like phishing and malware can target data that is stored in a large number of files. These massive amounts of data could be misused, which could seriously harm the organizations. A local server is incapable of storing a large amount of data. Thus, the use of clouds is considered. Users no longer need to purchase expensive gear thanks to the groundbreaking architecture of cloud computing; however, the rapid adoption of clouds poses various security issues Hacking has become such a major issue in recent years that cloud data security is the main issue. AES, DES, and RSA are only a few of the many algorithms that have been tried and failed to safeguard data. As a result, this study has proposed a method for protecting critical data files and also proposed an algorithm to secure communication and protect data from unauthorized access.
Image Captioning Using Python

In the last few years, the problem of recognizing the objects and the context of the image has gained a rising interest. Image Captioning is a task of recognizing the context of the image and then generating a caption for it with proper grammar structure. Generating captions automatically will be helpful for visually impaired people to understand the picture better. To achieve this task a hybrid model is defined in this paper which uses CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory). The model will be trained using Flickr8K data sets with containing 8000 images and 5 captions for each image.
Content-aware QoE optimization in MEC-assisted Mobile video streaming

Abstract The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%–12.3%, 8%–65%, 3.3%–5.7%, and 60%–130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.
HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks

Abstract Online social network datasets contain a large amount of various information about their users. Preserving users’ privacy while publishing or sharing datasets with third parties has become a challenging problem. The k -automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k -anonymity methods, including k -degree or k -neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k -automorphism one. The proposed algorithm is based on the structure of the previously published k -automorphism KM algorithm. However, it solves the NP-hard subtask of finding isomorphic graph extensions with a genetic algorithm and employs the GraMi algorithm for finding frequent subgraphs. In the design of the genetic algorithm, we introduce the novel chromosome representation in which the length of the chromosome is independent of the size of the input network, and each individual in each generation leads to the k -automorphism solution. Moreover, we present a heuristic method for selecting the set of vertex disjoint subgraphs. To test the algorithm, we run experiments on a set of real social networks and use the SecGraph tool to evaluate our results in terms of protection against deanonymization attacks and preserving data utility. It makes our experimental results comparable with any future research.
The Role of Digitalization in the Scientific and Educational Space Integration of the EAEU

The article analyzes the modern potential of education and science in the EAEU countries for the rapid and effective implementation of high-tech import substitution, and also builds econometric models of the impact of education and science on GDP growth on two panels and historical data for 1990-2020 for developed and developing countries, with their subsequent application to assess the impact of the unified scientific and educational space of the EAEU on economic growth with the identification of the main factors of such influence, which made it possible to formulate the most promising ways and mechanisms for creating a unified scientific and educational space of the EAEU.
Optical implementation and robustness validation for multi-scale masked autoencoder

Masked Autoencoders (MAEs), the state-of-the-art self-supervised neural network architecture in miscellaneous vision tasks, show surprisingly effective potential in reconstructing images distorted by random masking. This paper first introduces an optical implementation of MAEs, employing digital micromirror devices in the optical path to capture partially blocked images. MAEs with multi-scale patches are deployed in the reconstruction procedure. By using an optical-specialized version of the reconstruction network, the system can reconstruct original scenes of high quality. Simulations and experimental measurements showed a significant performance, achieving 24.41 dB average peak-signal-to-noise on Davis2017 datasets and 29.92 dB (masked areas) on authentic captured images under 70% of pixels being blocked. This paves the way for the application of low-bandwidth sampling of high-throughput high-resolution images.
Vietnamese Sentiment Analysis: An Overview and Comparative Study of Fine-tuning Pretrained Language Models

Sentiment Analysis (SA) is one of the most active research areas in the Natural Language Processing (NLP) field due to its potential for business and society. With the development of language representation models, numerous methods have shown promising efficiency in fine-tuning pre-trained language models in NLP downstream tasks. For Vietnamese, many available pre-trained language models were also released, including the monolingual and multilingual language models. Unfortunately, all of these models were trained on different architectures, pre-trained data, and pre-processing steps; consequently, fine-tuning these models can be expected to yield different effectiveness. In addition, there is no study focusing on evaluating the performance of these models on the same datasets for the SA task up to now. This article presents a fine-tuning approach to investigate the performance of different pre-trained language models for the Vietnamese SA task. The experimental results show the superior performance of the monolingual PhoBERT model and ViT5 model in comparison with previous studies and provide new state-of-the-art performances on five benchmark Vietnamese SA datasets. To the best of our knowledge, our study is the first attempt to investigate the performance of fine-tuning Transformer-based models on five datasets with different domains and sizes for the Vietnamese SA task.
Learning semantic ambiguities for zero-shot learning

Abstract Zero-shot learning (ZSL) aims at recognizing classes for which no visual sample is available at training time. To address this issue, one can rely on a semantic description of each class. A typical ZSL model learns a mapping between the visual samples of seen classes and the corresponding semantic descriptions, in order to do the same on unseen classes at test time. State of the art approaches rely on generative models that synthesize visual features from the prototype of a class, such that a classifier can then be learned in a supervised manner. However, these approaches are usually biased towards seen classes whose visual instances are the only one that can be matched to a given class prototype. We propose a regularization method that can be applied to any conditional generative-based ZSL method, by leveraging only the semantic class prototypes. It learns to synthesize discriminative features for possible semantic description that are not available at training time, that is the unseen ones. The approach is evaluated for ZSL and GZSL on four datasets commonly used in the literature, either in inductive or transductive settings, with results on-par or above state of the art approaches. The code is available at https://github.com/hanouticelina/lsa-zsl .