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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT 21

Empowering Low-Resource Language Translation: Methodologies ...

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21st International Conference on Spoken Language Translation, IWSLT 2024

作者： Anand, Harpreet Singh Dash, Amulya Ratna Sharma, Yashvardhan Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798891761414

This paper presents the methodologies implemented for the Automatic Speech Recognition and Machine Translation for the language pairs Bhojpuri-Hindi and Marathi-Hindi for the Dialectal and Low-Resource shared task proposed by The International Conference on Spoken Language Translation (IWSLT) for 2024. The implemented method uses the transcriptions generated through a fine-tuned Whisper models(for Marathi-Hindi) and vakyansh-wav2vec model (for Bhojpuri-Hindi) and generates the translations using fine-tuned NLLB(No Language Left Behind) Models for both the tasks. The selection of more accurate translation is done through sentence-embeddings generated using the MuRIL(Multilingual Representations for Indian Languages)(Khanuja et al., 2021) model for the Marathi-Hindi task. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saha, Piyush Ghosh, Sumon Bhandari, Dinabandhu Calcutta Institute of Engineering and Management Dept. of Electrical Engineering Kolkata India Heritage Institute of Technology Dept. of Computer Applications Kolkata India Heritage Institute of Technology Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350314144

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field. © 2023 IEEE.

关键词： Generative adversarial networks

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Cost-aware and Time-Varying Request Rate-based Dynamic Cache Provisioning over CCDN 17

Cost-aware and Time-Varying Request Rate-based Dynamic Cache...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Malaviya National Institute of Technology Jaipur Manipal University Jaipur Dept. of Computer Science & Engineering India Malaviya National Institute of Technology Jaipur Dept. of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350307672

Today's Internet users depend on Cloud-based Content Delivery Network (CCDN) providers to efficiently use resources and minimize content delivery latency. However, time-varying request rates make incoming requests unpredictable, resulting in higher miss rates in CCDNs. Thus, dynamically allocating cache instances to mitigate request rate spikes is necessary to maintain Quality of Service (QoS). Therefore, by utilizing the Request Count Window (RCW) cache and a dynamic instantiation mechanism, this article aims to efficiently manage a changing request window and store objects according to a caching policy. To improve cache performance, we propose a new dynamic selective caching policy that caches only the most frequently requested objects over the past L requests. To further enhance dynamic cache provisioning and reduce the duration of resource allocation, a variable time slot mechanism based on the current request rate is introduced. Finally, we present a cost model that accounts for caching and delivery costs due to missed occurrences. The proposed method is extensively compared with the Least Recently Used (LRU) caching policy and the RCW cache with selective caching based on Kth request cache insertion policies. Experimental results demonstrate that the proposed method outperforms the LRU, selective caching on the values 1 and 2 of K by 22.04%, 12.11%, and 9.31%, and by 29.50%, 16.71%, and 3.03% in terms of the average total cost using permanent cache sizes of 2 and 5, respectively. © 2023 IEEE.

关键词： Dynamics

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A Semantic Driven Framework for Energy Efficient Task Scheduling in Dynamic Cloud Environment 4

A Semantic Driven Framework for Energy Efficient Task Schedu...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aarav Kannan, J. Amshavalli, R.S. Venkateswara Reddy, D. Varma, Vegesna Surya Vinay Kavitha, G. Srm Institute of Science and Technology Dept of Biomedical Engineering Chennai India Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges, including suboptimal energy efficiency, limited adaptability to dynamic cloud environments, and insufficient context-aware decision-making. Recent techniques, such as Meta Reinforcement Learning, Genetic Algorithms, and Energy-Aware Scheduling, show potential but often struggle with high computational complexity, static resource allocation, or scalability issues. These shortcomings result in inefficient resource utilization, elevated energy usage, and difficulties in meeting Service Level Agreement (SLA) requirements. To address these challenges, this study introduces the Energy Efficient Semantic Scheduling Framework (ESSF), a novel approach combining semantic reasoning and energy profiling to optimize task scheduling in dynamic cloud environments. The framework integrates three key components: (1) Dynamic Energy Profiling (DEP) for real-time energy monitoring, (2) Semantic Resource Matching (SRM) utilizing knowledge graphs for context-aware allocation, and (3) Adaptive Workload Balancing (AWB) to dynamically redistribute tasks for improved resource utilization and energy efficiency. Experiments using the Google Cluster Trace Dataset demonstrate that ESSF reduces energy consumption by up to 25% compared to traditional methods while achieving 90% resource utilization and 97% SLA compliance. These results highlight the potential of the proposed framework to overcome existing limitations, offering a scalable, adaptive, and environmentally sustainable solution for task scheduling in cloud computing. © 2025 IEEE.

关键词： Cloud Computing Dynamic Energy Profiling Energy Efficiency Google Cluster Trace Dataset Knowledge Graphs Semantic Scheduling

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An Ensemble Learning Approach to Detect the Presence of Diabetic Retinopathy based on Inception Module 2

An Ensemble Learning Approach to Detect the Presence of Diab...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Devi, T. Aswini Yamana, Sathwik Harshankar Reddy, G. Sangita, Vedh Sivaji Kumar Kulkarni, Swapneel Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665456302

In recent times, Diabetic Retinopathy (DR) has become a complication of major degree for diabetic patients, in which the blood vessels present in the eye retina are severely damaged. This in most cases has lead to a loss of vision as observed from records of DR patients, and if left untreated, can cause blindness. As per the estimates of the World Health Organization, DR will impact around 224 million people by 2040. This research study presents a convolutional neural network (CNN) model, followed by employing ensemble learning over various ML algorithms through max voting, for the task of image classification for detecting Diabetic Retinopathy using color fundus images, with the goal of improving the accuracy and efficiency of the diagnostic process. The proposed model is designed to address the challenges of real-world data, such as variability in the appearance of images that belong to the same class and class imbalance. The dataset used for this study is EYEPACS, which is widely used for training and testing models for detecting Diabetic Retinopathy. The Metrics that are used for measuring the performance of the proposed model are accuracy, precision, and F1-S core. This study obtains an accuracy of 95.4 percent. © 2023 IEEE.

关键词： Blood vessels

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Mishra, Amit Kumar Pandey, Neeraj Kumar Diwakar, Manoj Bahadure, Nilesh Bhaskarrao Dumka, Ankur Patni, Jagdish Chandra Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Dept. of CSE Nagpur Campus Pune India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of CSE Karnataka Bengaluru India

ISBN: (纸本)9798350394412

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate. © 2024 IEEE.

关键词： Radial basis function networks

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System 6

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Liao, Jing Wu, Yadong Wang, Guijuan Wang, Fupan Chen, Huarong Long, Li Southwest University of Science and Technology Dept. Computer Science and Technology Mianyang China Sichuan University of Science and Engineering Dept. Computer Science and Engineering Yibin China

ISBN: (纸本)9798350325485

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint. © 2023 IEEE.

关键词： Decision trees

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Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals 5

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

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5th International Conference on Biomedical engineering, IBIOMED 2024

作者： Prajapati, Vaibhav Turlapaty, Anish C. Sarma, Himangshu Ghorai, Mrinmoy Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Sri City India Indian Institute of Information Technology Dept. of Computer Science and Engineering Sri City India

ISBN: (纸本)9798350389265

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance. © 2024 IEEE.

关键词： Decision trees

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