Reviewerly/exHarmony huggingface dataset:
Investigating the Impact of Containerization on the Deployment Process in DevOps

This research investigates the impact of containerization on the deployment process in DevOps. Containerization is a technology that enables developers to package their applications and dependencies into lightweight, portable containers that can run anywhere. This study aims to explore how containerization affects various aspects of the deployment process, such as speed, consistency, portability, scalability, and security. Through a comprehensive review of existing literature and case studies, the study identifies the benefits and challenges of using containers in DevOps and highlights best practices and security considerations for organizations adopting containerization. The research findings provide insights into how containerization can improve the deployment process in DevOps, enabling organizations to make informed decisions about incorporating containerization into their software development and deployment workflows.
Offline Handwriting Recognition of Thai Characters Using Multiple Deep Neural Networks

Handwritten character recognition is a challenge task in many languages. However, languages have their own characteristics. In Thai language, although it is always written from left to right in horizontal direction, the character alignment can be up to four levels in a vertical direction, which is different from several languages such as Chinese, English, Japanese, etc. This work proposed a method for offline handwritten recognition in Thai language based on transfer learning. In this work, we used VGGNet-16 as a pretrained network. The system contains two separate networks for recognizing the characters at base level and the remaining levels which are segmented from a word. Although the character spacing in a word still must be kept enough due to the problems of the segmentation of overlapping characters and the ambiguity of some combinations of adjacent characters in Thai scripts. The proposed method performed quite high accuracies in Thai handwritten recognition.
Scene Style Conversion Algorithm of AI Digital Host: A Deep Learning Approach

In the recent years, image diversity generation algorithms based on deep learning have gradually mined feature information that can describe the essential content of images through hierarchical learning, which has become a current research hotspot. In particular, the emergence of GANs has made the qualitative leap in the speed and quality of image data generation due to its good autonomous generation ability and ingenious weakly supervised learning mode. In this study, the focus is deep learning based scene style conversion algorithm of the AI digital host. The proposed model has 2 aspects: (1) Efficient face modelling. In this step, the face features are extracted and combined with deep neural networks to obtain the efficient representations, then, the rough stitching of point cloud data is applied to construct the different perspectives the faces. (2) Efficient style conversion algorithm. This study designs an improved GAN algorithm to create new image conversion scheme to complete the design of the AI host. In the experiment section, the mainstream evaluation methods are adopted to test the performance.
Saturated Neighborhood Graph Clustering Optimization Algorithm Based on Edge Information

Clustering is a crucial task in the field of data mining. However, existing clustering algorithms often suffer from parameter dependence, which poses a challenge for users who need to possess a certain level of knowledge to effectively utilize these algorithms. This requirement raises the threshold for applying such algorithms. Furthermore, different clustering algorithms have varying parameter requirements, and the appropriate parameters may differ across different datasets. The introduction of non-parameter algorithms offers a promising solution to the criticized parameter tuning problem. In this study, some scholars have proposed the Saturated Neighborhood Graph (SNGC) clustering algorithm, which demonstrates excellent clustering effectiveness and possesses the characteristics of being non-parameter and robust. This study aims to optimize the clustering process of the SNGC algorithm and proposes a new clustering algorithm called SNGCE. The innovations of this research primarily encompass the following two points: 1) It addresses the issue of unconstrained during the construction of the saturated neighborhood graph; 2) It presents a novel approach to boundary detection within the clustering field. Experimental data indicates that the incorporation of boundary point information in this study can enhance the results of the clustering algorithm to a certain extent.
Quality of Experience Assessment for Free-viewpoint Video

The evaluation of free-viewpoint video (FVV) quality is essential for improving the quality of experience (QoE). Prior deep video quality assessment (VQA) approaches for FVV typically focused on either spatial or temporal distortions and lacked a comprehensive assessment considering the two aspects. In this paper, we provide an end-to-end no-reference video quality assessment (NRVQA) model for FVV that predicts video quality scores based on both spatial and temporal features. It consists of a spatial feature perception module, a temporal motion feature perception module and a quality score fusion module. In order to provide a quality score that is highly relevant to the mean opinion score (MOS) from the subjective quality assessment experiment, the quality-related features in the spatial and temporal domains of FVV are effectively utilized and merged. Experimental results show that the PLCC and SRCC improved by 25.0% and 18.5%, respectively, compared to state-of-the-art method. Moreover, the ablation experiments demonstrate the importance of both spatial features and temporal motion features.
Frequency-domain Transformer-based Low-light Image Enhancement Network

During the process of capturing images under low-light conditions, images often suffer from issues such as low contrast and high noise due to the limitations of illumination. Recently, with the rapid development of transformers, they have shown significant effectiveness in low-light image enhancement tasks. Therefore, we propose a low-light image enhancement network based on the frequency-domain transformer (FDT). This network consists of a main network and a frequency-domain auxiliary network. In the main network, we use the proposed frequency-domain transformer (FDT) to distinguish between high and low-frequency domains and selectively preserve the required high and low-frequency information. In the frequency-domain auxiliary network, the frequency-domain enhancement module (FDEM) is used to extract features and aggregate with the main network, to achieve the purpose of assisting the integration of information from the main network. Extensive experimental results demonstrate that our proposed method outperforms existing methods to a large extent.
Synergistic Detection of SMS Spam: Harnessing the Power of Hybrid Voting Technique

Due to the variety of spamming techniques used, detecting SMS spam is a difficult task. This research study suggests a novel approach to improving SMS spam detection accuracy by leveraging the power of hybrid voting techniques. This research aims to combine the outputs of various machine learning models. Experiment results on a publicly available dataset show that the proposed hybrid voting technique outperforms individual models, detecting SMS spam with a high accuracy of over 98%. This approach has a lot of potential for improving SMS spam detection and can be applied to other types of spam detection tasks in different domains.
Knowledge Tracing Modeling Method for Programming Task

Knowledge Tracing is used to assess student learning progress and predict student performance. It analyzes the student's learning history to infer the degree to which the student has mastered the knowledge points. Existing knowledge tracking models often have various limitations. First, most knowledge tracking models take question number and student's binary answer as the input mode, ignoring other information of the student's answer, and second, most KT models are based on few attempts, so the current KT model cannot adapt to More complex answering situations in multiple task. This paper studies the KT model in the field of computer education. By analyzing the characteristics of the programming task KT data set, a modeling method for programming task is proposed. First, we got the students' accurate score information based on the code text's similarity. Then, we extracted student's multiple attempt sequence features based on the sliding window, which improved the model's ability to solve long-sequence problems. Experiments on three standard KT models showed that this modeling method effectively improved model performance. model performance.
Research on Multimedia Information Processing System based on Data Mining Technology

This scholarly paper presents the main findings of an in-depth exploration of multimedia data mining, focusing on the extraction and organization of metadata from expansive multimedia datasets. The study employs a multidimensional analysis approach using multimedia data cubes, which provide a comprehensive representation of the data. The paper emphasizes the importance of result presentation and advocates for the use of visualization techniques to effectively convey mining outcomes. Various techniques in multimedia mining are examined, including text data mining for extracting meaningful patterns from multimedia text data, and image data mining for discovering significant patterns from multimedia image data. Video data mining is also explored, involving the automatic extraction of content, structural features, and semantic information from video objects. The methodologies employed in video mining encompass statistics, classification, clustering, association, and trend analysis. The paper concludes by discussing system design, aiming to optimize the multimedia information processing system based on data mining technology. This comprehensive approach ensures the seamless integration of various data mining techniques, enhancing the efficiency and effectiveness of multimedia data analysis.
Distilling Object Detection Fusion of Foreground Enhancement and Spatial Frequency

Current knowledge distillation methods used in object detection often ignore the importance of global information in object detection and the quantity of foreground and background samples is unbalanced in the process of object detection training. Aiming at the above problems, a knowledge distillation method for object detection based on foreground enhancement and spatial frequency is proposed. This method uses the foreground enhancement module to introduce the attention mechanism to enhance the expression ability of the key region in the backbone feature, forcing the student model and the teacher model to focus on the same key region. The spatial frequency module guides the student model to inherit the global information contained in the teacher network by using the property of fast Fourier convolution. The losses of the neck network classifier and the regressor are calculated separately. Finally, all losses are fed into the full connection layer to obtain the final distillation loss. Experiments show that the proposed method can be applied to different object detection frameworks. Faster R-CNN, RetinaNet, and FCOS with ResNet-50 as the backbone network obtained mAP of 40.8%, 39.9%, and 42.9% on the COCO dataset, respectively, an increase of 2.4%, 2.5%, and 4.4% compared with the benchmark.
Malicious Webpage Detection Based on Feature Fusion Using Natural Language Processing and Machine Learning

Malicious websites are purposefully designed to deceive internet users to steal sensitive personal information, infect the victim's system with malware, cause financial losses, and damage the victim's reputation. Finding these pages or links is hard for internet users. Such websites are discovered using detection tools. The majority of detection techniques use blacklisting or whitelisting strategies to find and prevent malicious websites. However, compiling such a sizable list of website links is a time-consuming job that is challenging to update regularly. Therefore, the researchers employ machine learning-based methods to identify these fraudulent connections. These methods are based on the features taken from URLs or web pages. Additionally, features such as DNS details, webpage reputation, and visual similarity data are used. However, these features are few and do not fully utilize the URLs or website contents. This work focuses on merging URL lexical features and content-based features for malicious webpage detection in order to fully exploit the dataset's potential. Natural language processing methods like Hashing, Count, and Term Frequency - Inverse Document Frequency (TF-IDF) vectorizers are employed to extract features from the content of Web pages. The suggested approach's efficiency is evaluated by using the most well-known machine learning methods. The outcome shows that the Count vectorizer with Random Forest achieves a higher accuracy of 91.17% with 500 features.
A Privacy-Preserved and Efficient Federated Learning Method Based on Important Data Selection

With the development of wireless communication technology, distributed machine learning is urgently needed to take advantage of the explosively heterogeneous data generated at the edge network. As a result, Federated Learning(FL) became the research focus since invented by Google in 2016. In this paper, a data selection algorithm based on data importance is proposed. By selecting the data with high importance each iteration instead of all the data, we could improve the computing efficiency and reduces the risk of privacy disclosure. At the same time, the optimization objectives are set as learning efficiency and total energy consumption of the system, so the Non--Dominated Sorting Genetic Algorithm (NSGA-II) based on elite strategy is adopted to solve the multi-objective optimization problem. Simulation results show that the proposed algorithm significantly improves the learning efficiency of the FL and greatly reduces the energy consumption.
Traveling Salesman Problem with Ant Colony Optimization

An application of Ant Colony Optimization (ACO) to the Travelling Salesman Problem (TSP) is presented in this research study. Ant Colony Optimization (ACO) is a novel technique for combinatorial optimization practitioners. Because ACO is based on the behavior of ant colonies, it has a significant advantage and a widely dispersed calculation mechanism. Finding optimization problems is rather easy using ACO. The “problem of the travelling salesman” is one of the most popular illustrations of combinatorial optimization. This study has used the Ant Colony Optimization (ACO) Algorithm to the Travelling Salesman Problem (TSP) in this work. This study also discusses about the most important modifications that might be made to the Ant Colony algorithm to improve its suitability for resolving the Travelling Salesman Problem (TSP) and ensuring the best results.
Smart Asset Management: Tracking and Optimizing Assets with IoT Sensors

Effective asset management requires keeping tabs on and better optimizing the usage of diverse assets to boost productivity and decrease costs. By providing real-time information on assets' whereabouts, conditions, and use, sensors linked to the Internet of Things (IoT) may revolutionize asset management. Sensors connected to the IoT are used in the context of asset management, with a focus on factories, warehouses, and distribution centers. Sensors may keep an eye on machinery to make sure it's working as it should, find problems before they become major, provide real-time data on stock and shipments, and increase efficiency. Data like this may be utilized to streamline processes, avoid problems, and save expenses, all of which boost productivity and delight customers. The potential benefits of IoT sensors in asset management are discussed. Reduced downtime, longer equipment lifespans, and streamlined supply chains are just a few of the possible advantages. Monitoring and optimizing assets using IoT-connected sensors may help businesses increase profits and provide better service to customers.
A Novel Survey on Panoptic Segmentation using Deep Learning Approaches

Image segmentation is a significant issue in computer vision and image processing, and many segmentation techniques are found in the literature. It has several applications, including robotic perception, augmented reality, medical image analysis, scene interpretation, photo compression and video surveillance. With Deep Learning (DL) becoming increasingly popular, new techniques for segmenting images with DL models have been created. The research discusses a variety of novel approaches in instance and semantic segmentation in this recently published literature review, which includes pyramid and multi-scale-based methods, convolutional pixel-labeling networks, generative and visual attention models and encoder-decoder architectures in competitive settings. This work examines some of the most well-known data sets, compares several DL-based segmentation models' advantages, disadvantages, and connections, assesses the outcomes, and discusses potential future research directions.
Text Feedback Classification using Machine Learning Techniques

The popularity of online shopping has grown worldwide, making it an integral part of many people's lives. As customers are free to express their emotions online, online sales have become a significant source of revenue. This enables obtaining honest feedback for various products, helping to understand not only what is popular but also the overall consensus. To make sense of the large amounts of product feedback and gauge the public's response, it is important to understand the widely held sentiments. Machine learning models provide a solution to extract feedback from text. Random Forest classifier produces the highest accuracy of 88 percentage.
An Approach to Perform Sentiment Analysis using Data Mining Algorithms

To perform the sentiment analysis as a basis for defining and extracting subjective information from sources or easily relating to the identification phase of the polarity of the text, the concept of Natural Processing is used. Participatory approach is required to perform this analysis. It was also called opinion mining as it extracts a user's view or perspective. There are many attributes which pose a problem with knowledge. It is an arbitrary for choosing assets giving a wider range of values. In the current paper, various algorithms of classification are used and it is concluded that the best algorithm is random forest. The issue is that decision trees, especially if the tree is particularly deep, are vulnerable to being over fit. To minimize the bias and error of variance, classification along with random forest classification is used. Through practicing on different data sets, random forests minimize variance. In the proposed study, boosted methodology along with Random forest, instead of using only random forest is implemented due to which optimization of the Ant colony search alongside with the proposed classification to hit the classification for sentiment analysis of various reviews of films for research precision.
Federated Social Recommendation with Rényi Differential Privacy

A social recommendation system based on graph neural networks is a system that uses social relationships between users to generate personalized recommendations. To improve recommendation accuracy, it is usually necessary to collect a large amount of user behavior data, which may lead to user privacy breaches and data misuse. Existing privacy protection methods often sacrifice recommendation effectiveness or increase computational costs. Therefore, this article proposes a federated social recommendation system (FRSRec) based on Rényi differential privacy. By combining hash encryption technology with Rényi differential privacy, user data privacy can be protected while ensuring recommendation effectiveness and computational efficiency. This article conducted experimental evaluations on three real datasets. The experimental results show that the FRSRec model achieves optimal results in both MAE and RMSE, demonstrating that the FRSRec model can effectively achieve privacy protection while improving recommendation quality.
A Hybrid Cloud Security System using Cryptography

Information security is a major problem in cloud computing systems as the information may be physically placed anywhere in any data center throughout the networks. Due to the nature of cloud computing, user authentication, information integrity, and confidentiality are all brought up as significant challenges. In order to ensure confidentiality, integrity, and authentications while storing and accessing information from and to the data center as well as in peer contacts; it is advised to adopt a better, innovative, secure security method. Cloud security is a rapidly expanding area of computer and networks securities. In the cloud platforms paradigm, external data centers are used. It supports a range of programming languages used in the process of deploying web applications. The Cloud is built on a managed containers architecture that integrates information services and robust ecosystems for the deployment and operation of modern apps. In cloud computing, data securities are a major challenge that is dealt with using cryptographic methods. One potential method for encrypting data is the Advanced Encryption Standard (AES). After developing a cloud platform, the AES algorithm for encryption and the Data Encryption Standard (DES) method for decryption will be used in this work to secure data. The performance study suggests that DES and AES cryptography can be used to secure data. Calculations of data encryption and decryption delay also show that increased data delay periods are caused by bigger data volumes.
A Hybrid Machine Learning Approach for Document Classification: A Comparative Study

In the realm of Natural Language Processing (NLP), document categorization is a difficult problem since it requires assigning documents to preset groups based on their content. In this study, a novel method for document categorization that combines K-Means Clustering (KMC) with the Latent Dirichlet Allocation (LDA) algorithm was provided. Documents are first clustered using K-means clustering, and then latent topic modeling is used in each cluster to extract latent themes. Documents are sorted into their appropriate categories using the extracted subjects. The suggested technique on two open-source datasets and comparing the outcomes to those obtained by existing state-of-the-art approaches were tested. The outcomes from experiments proved the superior accuracy, precision, and recall of the suggested strategy over state-of-the-art approaches.