Reviewerly/exHarmony huggingface dataset:
An Enhanced Hybrid Deep Learning Model for Efficient Automatic Image Captioning

Image captioning is a field that overlaps natural language processing and computer vision from deep-learning algorithms. In order to provide a coherent description for high-level picture semantics, it is also essential to be able to analyze the characteristics and the relation between the objects. This paper focuses on developing an approach to creating a semantic image caption that makes maximal use of context and image cognition. To handle image captioning, Linking the detected objects with attention-enhanced deep-learning models is something that interests us significantly. The YOLOv3 is utilized to extract the image's features, and an extended Recurrent Neural Network, RNN (Long Short Term Memory, LSTM) with attention enrichment is then employed in order to construct the caption. We have utilized the attention mechanism to generate captions for images after considering the recognized items in the image scene. The COCO datasets are used for all four proposed variant models and the subset used for testing. To assess how well the various models performed, the semantic similarity analysis between produced descriptions and the actual image description is carried out. Finally, our results are compared to other traditional models.
Insights into an Emerging Software Methodology:DevOps

DevOps is an approach would that not only eradicates the endless series of concurrent activities but also maintain an effective Life Cycle of Software Development (SDLC). DevOps is characterized by fewer errors, more efficiency, and remarkable coordination. The goal of this paper is to establish the base as to what DevOps is and its benefits for current industries, and in what sectors we predict it to be further used in the forthcoming future. While also taking one important aspect of DevOps into perspective- Continuous Integration and Continuous Deployment (CI/CD), this paper highlights pipeline principles and automation along with DevOps tools/ enablers and the workflow/ lifecycle of DevOps.
Modeling and Estimation Methods for Student Achievement Recognition Based on XGBoost Algorithm

With the development of deep learning related algorithms, the target requirements of performance recognition models are constantly being optimized. The research on student performance recognition modeling and estimation methods based on XGBoost (Extreme Gradient Boosting) algorithm is becoming increasingly important. In the construction of the entire student achievement recognition and estimation model, how to improve the accuracy of the improved model’s achievement estimation and the efficiency of the system recognition is currently a key issue that needs to be urgently addressed. This article conducts research on traditional student performance prediction methods, utilizes the application of XGBoost algorithm in student performance, analyzes the algorithm process steps, and combines the performance evaluation system of student performance classification. Based on the data results, the following conclusions are drawn through discussion. Six subject samples were selected through simulation experiments, and the student achievement recognition and estimation model based on XGBoost algorithm was applied to improve the accuracy of achievement estimation compared to traditional schemes, with a comprehensive average improvement of about 8.4%. At the same time, there have been improvements in the efficiency of system identification, and the overall average improvement is about 17.8%. This indicates that the student achievement recognition and estimation model based on XGBoost algorithm has good practical application effect in predicting student achievement. Finally, this study also provides data-based decision-making support for education policy makers, helping to optimize the allocation of educational resources, improve education quality, promote academic success and personal development of students, and thus contribute important methods and tools to the improvement and enhancement of the education system.
Modi Script Recognition Using Convolutional Neural Networks and VGG16: A Deep Learning Approach for Historical Script Analysis

Recognizing historical scripts is a challenging task that requires specialized techniques due to the unique characteristics and variations present in these scripts. The Modi script, a historic script used to write the Marathi language in India, is presented in this research as a deep learning solution for the recognition of the script utilizing convolutional neural networks (CNNs) and the VGG16 architecture. The proposed approach consists of several steps. First, a diverse dataset of Modi script samples is collected, encompassing various characters, words, and sentences written in the script. The dataset is preprocessed to ensure consistency and enhance the model's robustness, including resizing, grayscale conversion, normalization, and data augmentation techniques. After that, the dataset is partitioned into three sets: training, validation, and testing. The VGG16 architecture is employed as the base model for feature extraction, leveraging its ability to learn low-level and high-level features from images. By obtaining handwritten examples from the IEEE Dataport collection, the dataset (training images) for the characters is updated. The model is trained using the training dataset, and hyperparameter tuning is performed using the validation set to optimize its performance. The testing dataset is used to measure the trained model's performance in identifying the Modi script by assessing metrics including accuracy, precision, recall, and F1 score. The performance of the model is assessed, and any updates or modifications that are required to increase the model's accuracy are implemented. The developed model can be deployed for prediction on unseen Modi script images, enabling the recognition of characters or words within the script. This approach provides a promising solution for historical script analysis and contributes to the preservation and understanding of the Modi script's cultural and linguistic heritage.
Real-Time Hand Gesture Recognition with Voice Conversion for Deaf and Dumb

Individuals primarily communicate with one another through various means, such as verbal language, written language, or nonverbal cues. However, for those who are deaf and dumb, the only mode of communication is sign language. Unfortunately, communication becomes difficult if such people are unaware of sign language, leading to frustration and a lack of ability to express emotions effectively. In emergency situations, the inability to communicate effectively can be particularly challenging. Researchers have looked exploring ways to translate hand motions into text and sounds in order to find a solution around this problem. Both vision-based and non-vision-based methods are frequently employed to identify hand motions or gestures. The former uses sensors, while the latter uses cameras for gesture detection. This study focuses on a vision-based approach and develops a gesture recognition system using artificial neural networks. The system locates and recognizes hand movements, allowing for communication to be maintained with others. The benefits and drawbacks of hand movement recognition are also examined. Overall, this research aims to address the communication barriers faced by those who rely on sign language and to improve their ability to communicate effectively in emergency situations.
Review of Reinforcement Learning in Chrome Dino Game

The intention of this study is to study some implementation of reinforcement learning in the environment of Google Chrome's Dino Game. Different environments were tested on Deep Q network and proximal policy optimization models. Images were provided as inputs in all trials and the highest scores were recorded and analysed to check which algorithm and environment performed better. Further work aims at including more environments and algorithms and performing training on High performance Computers.
Research on word difficulty classification based on machine learning

To classify the solution words according to difficulty and determine the attributes of the given words, a model is developed. The model is used to classify the word "EERIE", and the accuracy of the classification is discussed. In this study, the average percentage distribution of unequal weights is used to quantify the number of words divided into three levels: easy, medium, and difficult, and then tagged. In this study, the radical support vector machine model with radial basis function kernel is used, the accuracy is 0.6556 and the F1 value is 0.6634. In addition, the classification result of EERIE is difficult. This study also found some interesting observations from the data set, one of which discussed the difference between human thinking and machine learning.
Enhancing Hand Gesture Recognition with Media Pipe and SVM Model on Custom Dataset

Gesture recognition is an important area of research with many potential applications such as human-computer interaction, language recognition, and gaming. In this research paper a new approach to motion recognition using the Media Pipe and SVM models has been explained. The approach includes preprocessing the video motion input, using Media Pipe to extract relevant features, and using an SVM model to classify the annotation and to evaluate the proposed methods in public information and demonstrate the effectiveness of the plan by achieving high accuracy.
User-guided Page Merging for Memory Deduplication in Serverless Systems

Serverless computing is an emerging cloud paradigm that offers an elastic and scalable allocation of computing resources with pay-as-you-go billing. In the Function-as-a-Service (FaaS) programming model, applications comprise short-lived and stateless serverless functions executed in isolated containers or microVMs, which can quickly scale to thousands of instances and process terabytes of data. This flexibility comes at the cost of duplicated runtimes, libraries, and user data spread across many function instances, and cloud providers do not utilize this redundancy. The memory footprint of serverless forces removing idle containers to make space for new ones, which decreases performance through more cold starts and fewer data caching opportunities.We address this issue by proposing deduplicating memory pages of serverless workers with identical content, based on the content-based page-sharing concept of Linux Kernel Same-page Merging (KSM). We replace the background memory scanning process of KSM, as it is too slow to locate sharing candidates in short-lived functions. Instead, we design User-Guided Page Merging (UPM), a built-in Linux kernel module that leverages the madvise system call: we enable users to advise the kernel of memory areas that can be shared with others. We show that UPM reduces memory consumption by up to 55% on 16 concurrent containers executing a typical image recognition function, more than doubling the density for containers of the same function that can run on a system.
SBTREC - A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis

When traveling to an unfamiliar city for holidays, tourists often rely on guidebooks, travel websites, or recommendation systems to plan their daily itineraries and explore popular points of interest (POIS). However, these approaches may lack optimization in terms of time feasibility, localities, and user preferences. In this paper, we propose the SBTREC algorithm: a BERT-based Trajectory Recommendation with sentiment analysis, for recommending personalized sequences of POIS as itineraries. Considering the locations, sightseeing, and travel time between consecutive Pots, our approach incorporates individual user preferences through the utilization of historical data. The key contributions of this work include analyzing users’ check-ins and uploaded photos to understand the relationship between Pot visits and distance. In addition, SBTREC also encompasses sentiment analysis to improve recommendation accuracy by understanding users’ preferences and satisfaction levels from reviews and comments about different Pots. Our proposed algorithms are evaluated against other sequence prediction methods using datasets from 8 cities. The results demonstrate that SBTREC achieves an average $\mathcal{F}_{1}$ score of 61.45%, outperforming baseline algorithms. The paper further discusses the flexibility of the SBTREC algorithm, its ability to adapt to different scenarios and cities without modification, and its potential for extension by incorporating additional information for more reliable predictions. Overall, SBTREC provides personalized and relevant Pot recommendations, enhancing tourists’ overall trip experiences. Future work includes fine-tuning personalized embeddings for users, with evaluation of users’ comments on Pots, to further enhance prediction accuracy.
The Application of Long Short-Term Memory and Transformers for Music Generation

Music is embedded deeply in our well-being, and it has a great impact on improving mood, decreasing pain and anxiety, and providing opportunities to express oneself emotionally. Upon hearing a familiar tune, the human body releases a neurotransmitter called Dopamine which is responsible for the feeling of pleasure and comfort. Research suggests that music helps the brain cells to process information more effectively. Studies prove that people who suffered from stroke gained back their cognition within two months by listening to music when compared to the ones who did not listen to music. Music comprises numerous elements like pitch, rhythm, tone, etc. Music is subjective and broadly classified by genre. Music is also shown to have health benefits, both physically and emotionally. With advancements in generative AI, there is a unique opportunity to supply new and fresh tunes using knowledge of the type of music a patient listens to. In this paper, we present a deep-learning technique to create music samples. We experiment with Long Short-Term Memory (LSTM) and transformers in generating music. We envision that our platform will generate novel music samples of a particular genre and in the long-term help patients in need of physical and emotional strength.
Construction of an English Translation Intelligent Recognition System Based on BP Neural Algorithm

Intelligent English translation automatically inputs, processes, transmits and outputs English information by means of speech recognition and natural-language understanding. Most of the methods currently used are based on machine learning algorithms, but there are many defects in training, resulting in low translation efficiency and accuracy. This article introduced the BP (back propagation) neural network into the process of English translation recognition, constructed an intelligent model that can achieve online fast classification, and verified through experiments that the model had good predictive ability and generalization, providing new ideas and technical approaches for solving English vocabulary retrieval problems. Comparing the translation system based on BP neural network with the system based on Support Vector Machine (SVM), the recognition results showed that the average recognition accuracy of the system based on BP neural network for translated text was about 3.42% higher than that of the system using SVM. In practical applications, it can effectively improve English recognition speed and enhance user experience. The BP neural algorithm overcomes the shortcomings of traditional artificial neural networks, such as high computational complexity and being prone to falling into local extreme points, and provides an efficient and practical solution for the field of English translation.
Exploring FMU-NET: A Comprehensive Study on Person Identification Through Novel Semantic Segmentation Techniques

The paper proposes novel face semantic segmentation for person identification. Various face segmentation procedures are used to analyze the accuracy of the model to learn specific patterns from the annotated masks. The FMU- Net, a simplistic semantic segmentation model, amalgamates components from both the Residual U-Net and Mobile U-Net architectures. It adeptly capitalizes on the strengths of these frameworks while honing in on extracting precise facial features from newly devised masks. This meticulous feature extraction strategy empowers the FMU-Net to surpass its counterparts, as evidenced by the comprehensive comparative analysis presented in the paper. In the initial phase, semantic maps serve as the foundation for training the model, and subsequently, test semantic maps are generated and juxtaposed across various neural network architectures. The second stage of the methodology encompasses the integration of a classification layer with the semantic segmentation model. This combined approach not only delineates semantic segments but also discerns the individual’s identity, achieved through rigorous evaluation metrics such as the F1 score. Central to this research is the quest for the optimal neural network architecture tailored for semantic segmentation in the realm of face recognition. The study delves into intricate nuances of semantic face recognition, wherein the extraction of nuanced facial features plays a pivotal role. By strategically employing semantic maps and leveraging the fusion of proven architectural elements, the FMU-Net stands as a testament to the advancements in this domain, showcasing its prowess in discerning intricate facial details for robust and accurate face recognition applications. Additionally, the paper aims to enhance the chosen segmentation model by improving its time efficiency and reducing its complexity. This is achieved through the utilization of novel annotated semantic masks, which provide valuable insights for modifying and optimizing the segmentation model.
Automated MOM Generator with Attendance Monitoring

To automate the process of scripting the minutes of meetings and monitoring the attendance of meeting attendees. Minutes of the meeting contains revised information about various decisions taken, discussions done on many agendas, the final abstract of the meeting and the attendance of attendee's present in the meeting. Generating the Meeting minutes document is a manual and time-consuming process. We are proposing a solution to automate the process of scripting minutes of meetings which is currently done manually and keep track of attendees of meetings. This would reduce the effort of generating minutes document manually and distributing it among attendees of meeting. This project is divided into 3 modules the first module would be about converting meeting recorded audio to text, the second module would focus on text summarization which would include objectives, agendas, date and time of the meeting, decisions, and issues discussed in meeting, and the third module would focus on getting the attendance of meetings participants. Our proposed hardware device would be able to capture long range voice as it would be using sound shark mic, after which it would process and convert this recorded meeting audio to text and summary of this text would be generated with inbuilt machine learning model with help of embedded chip. This hardware device would contain a camera to capture faces of meeting attendees which would be passed to face recognition model for monitoring the attendance of meeting attendees. After the meeting MOM document would be generated and sent via mail to admin/host.
Comparative Analysis of Image Deblurring Deep Networks

It is often observed that most of the commonly captured pictures are acquired using the mobile cameras or the CCTV surveillance cameras. These mobile cameras capture the video footages of the activities of people who are stationary or in motion. These footages are often used as the evidence for different criminal cases which need to be restored for the proper information acquisition. Towards this deep research is happening to overcome the various issues influencing the quality of the video frames. We propose to eliminate blur and restore blurred images that are captured by different camera devices using GAN network. In this work we proposed a novel architecture and the performance is compared with pre-existing GAN models to overcome the issues of blurness in the captured video frames. The proposed solution promises to perform better for the restoration of the various types of video frames namely facial, scenaries, crowded areas, etc. from various noises.
A Rewiring Contrastive Patch PerformerMixer Framework for Graph Representation Learning

Integrating transformers with graph representation learning has emerged as a research focal point. However, recent studies showed that positional encoding in Transformers does not capture enough structural information between nodes. Additionally, existing graph neural network (GNN) models face the oversquashing issue, impeding information retention from distant nodes. To address, we transform graphs into regular structures, such as tokens, to enhance positional understanding and leverage transformer strengths. Inspired by the visual transformer (ViT) model, we propose partitioning graphs into patches and apply GNN models obtain fixed size vectors. Notably, our approach adopts contrastive learning for in-depth graph structure and incorporate more topological information via Ricci curvature to alleviate over-squashing problem by attenuating the effects of negatively curved edges while preserving the original graph structure. Unlike existing graph rewiring methods that directly modify graph structure by adding or removing edges, this approach is potentially more suitable for applications such as molecular learning where structural preservation is important. Our innovative pipeline subsequently introduces the PerformerMixer, a transformer variant with linear complexity, ensuring efficient computation. Evaluations on real-world benchmarks demonstrate our framework’s superior performance, like Peptides-func and achieve 3-WL expressiveness.
Semi-supervised 3D Video Information Retrieval with Deep Neural Network and Bi-directional Dynamic-time Warping Algorithm

This paper presents a novel semi-supervised deep learning algorithm for retrieving similar 2D and 3D videos based on visual content. The proposed approach combines the power of deep convolutional and recurrent neural networks with dynamic time warping as a similarity measure. The proposed algorithm is designed to handle large video datasets and retrieve the most related videos to a given inquiry video clip based on its graphical frames and contents. We split both the candidate and the inquiry videos into a sequence of clips and convert each clip to a representation vector using an autoencoder-backed deep neural network. We then calculate a similarity measure between the sequences of embedding vectors using a bi-directional dynamic time-warping method. This approach is tested on multiple public datasets, including CC_WEB_VIDEO, Youtube-8m, S3DIS, and Synthia, and showed good results compared to state-of-the-art. The algorithm effectively solves video retrieval tasks and outperforms the benchmarked state-of-the-art deep learning model.