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Chitrarth: Bridging Vision and Language for a Billion People

Chitrarth: Bridging Vision and Language for a Billion People

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shaharukh Khan Ayush Tarun Abhinav Ravi Ali Faraz Praveen Kumar Pokala Anagha Bhangare Raja Kolla Chandra Khatri Shubham Agarwal Krutrim AI Bangalore India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent multimodal foundation models are primarily trained on English or high resource European language data, which limits their applicability to other medium and low-resource languages, such as the Indian languages. To address this limitation, we introduce Chitrarth (Chitra: Image; Artha: Meaning), an inclusive Vision-Language Model (VLM), specifically targeting the rich linguistic diversity and visual reasoning across 10 prominent Indian languages. Our model effectively integrates a state-of-the-art (SOTA) multilingual Large Language Model (LLM) with a vision module, primarily trained on multilingual image-text data. Furthermore, we also introduce BharatBench, a comprehensive framework for evaluating VLMs across various low resource languages, ultimately contributing to more diverse and effective AI systems. Our model presents SOTA results for benchmarks across Indian languages while retaining its efficiency in English. Through our research, we aim to set new benchmarks in multilingual-multimodal capabilities, offering substantial improvements over existing models and establishing a foundation for facilitating future advancements in this arena.

关键词： Training Representation learning visualization Pipelines Training data Benchmark testing data models Multilingual Speech processing Signal resolution

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Mechanical characterization of posture adjustment carts under the multi-posture condition of the ship block 2

Mechanical characterization of posture adjustment carts unde...

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2nd international conference on Frontiers of Applied Optics and Computer Engineering, AOCE 2025

作者： Wang, Guowei Hu, Qiushi Wang, Shuotian Han, Guo Li, Lei Zhao, Kelong School of Mechanical Engineering Jiangsu University of Science and Technology Zhenjiang212000 China

ISBN: (纸本)9781510689404

At present, shipbuilding mainly adopts the block construction method. The attitude adjustment trolley is crucial for the accurate and efficient docking of large ship blocks, and its performance directly affects the docking efficiency and accuracy. This paper first analyzes the structure and kinematics of the attitude adjustment trolley and studies its motion mode in detail. Then, finite element modeling and analysis are performed on the trolley to obtain the stress, displacement, and strain cloud diagrams. By applying different directions of force to the trolley, its working state is simulated in the block multi-attitude case. The results show that when the pressure is within the range of 25000N rated lifting force and the angle between the pressure direction and the horizontal plane does not exceed 15°, the maximum stress, displacement and strain of the trolley increase with the increase of the clamping angle. When the clamping angle reaches 15°, the maximum stress is 172 MPa, the maximum displacement is 0.9972 mm and the maximum strain is 0.002213. At the same time, as the angle increases, the location of the maximum stress and strain is shifted from the bottom of the slide table to the upper guide cap of the screw. © The Authors.

关键词： Finite element methods Clouds Design Head Process modeling Materials properties Safety Stress analysis visual analytics visualization

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Utilizing Machine Learning Techniques for Predictive Modeling and Early Detection of Chronic Diseases

Utilizing Machine Learning Techniques for Predictive Modelin...

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Smart Technologies, Communication and Robotics (STCR)

作者： Gayathri R Perarasi T Leeban Moses M Sukant C Department of ECE Bannari Amman Institute of Technology Sathyamangalam Tamilnadu Department of ECE Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

The increasing prevalence of chronic diseases necessitates innovative solutions for early prediction and management. This study develops a machine learning-based system for chronic disease prediction, leveraging real-time data inputs and advanced computational models. The system integrates predictive analytics with user-friendly visualization tools, enabling accurate and efficient assessments of health risks. Using a dataset encompassing key health indicators, such as age, medical history, lifestyle factors, and physiological metrics, the study implements algorithms including Random Forest, Support Vector Machines (SVM), and Neural Networks to identify patterns and predict disease outcomes. Embedded programming and a graphical user interface (GUI) were employed to enhance accessibility, allowing users to interact seamlessly with the system for real-time analysis. The proposed model demonstrates high accuracy, with a prediction rate exceeding 90% for chronic diseases like diabetes, cardiovascular conditions, and hypertension. These results highlight the system's potential for scalable applications in clinical decision support and personalized health management, emphasizing the transformative role of machine learning in modern healthcare.

关键词： Support vector machines Hypertension Accuracy Predictive models Real-time systems data models Predictive analytics Monitoring Diseases Graphical user interfaces

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Exploring Advanced Optimized Machine Learning Frameworks for Enhanced Analysis and Predictive Modeling of Drilling Well Production Performance

Exploring Advanced Optimized Machine Learning Frameworks for...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Md Ahammad Sharif K. Siva Kumar Venkata Ramana Avula Mohammed Huzaifa Sonu Kumar Emandi Krishna Sai Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The oil and gas industry is increasingly depending on advanced data analytics to maximize production and reduce costs. Perhaps the greatest difficult issue in this industry is precisely estimating the performance of drilling wells to improve decision-making processes. This paper provides ML techniques for predicting drilling well production using ensemble learning. To develop an accurate prediction system, the framework includes Exploratory data Analysis (EDA), data preprocessing, visualization, optimization, and model selection. In this work, combining RF with XGBoost aims to improve predictive performance through utilizing the capabilities of both algorithms. Firefly Optimization is utilized to tune hyperparameters in the hybrid model-building process. This hybrid model is specifically intended for error prediction in firefly optimization scenarios. The approach is developed in Python software and exhibits the real-world application of advanced ML techniques in industrial conditions. The proposed hybrid RF-XGBoost model exceeds in terms of error predicting for drilling well production, with an RMSE of 0.01, MAE of 0.01, and MSE of 0.10. The results indicate the approach is effectively accurate in predicting errors in complex data settings, providing insights that have a significant effect on operational decisions in the field of well data management.

关键词： Drilling Accuracy data analysis Production Predictive models Gas industry Software Petroleum industry Optimization Python

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A CLIP-based Video Question Answering framework with Explainable AI

A CLIP-based Video Question Answering framework with Explain...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Jeshmol P J Binsu C. Kovoor Division of Information Technology School of Engineering Cochin Univesity of Science and Technology Kochi Kerala

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Video analytics utilizing Video Question Answering (VideoQA) techniques to develop AI systems capable of comprehending and responding to user-specific questions from the content of the custom video datasets. The proposed system detects objects from the custom video datasets using YOLO v8, a deep learning-based object detection algorithm driven by the provided questions. The SORT algorithm helps to track detected objects throughout the entire video. Also, significant frames are extracted from the input questions utilizing a pre-trained model Contrastive Language-Image Pretraining (CLIP) specialized for analyzing video clips. The proposed model comprises video processing where the raw video data is analyzed and transformed for further analysis. Subsequently, questions are encoded and processed facilitating the integration of natural language understanding into the system. The proposed model integrates video comprehension with questions and enables it to correlate the content of the videos with the queries posed by the user. Finally, the model generates answers to the questions asked, accompanied by the selection of the most pertinent frame from the video based on similarity scores. The explainable LIME algorithm enhances the interpretability of the frame with the highest similarity score. This selection process ensures that the provided answers are grounded in the relevant visual context, enhancing the overall quality and relevance of the responses generated by the system. The model exhibited superior performance when applied to specialized custom video datasets.

关键词： YOLO Measurement Deep learning Computer science Analytical models Accuracy Explainable AI visual analytics Question answering (information retrieval) data models

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A Performance Model of In-Situ Techniques 33

A Performance Model of In-Situ Techniques

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33rd Euromicro international conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Ju, Yi Vidal, Nicolas Perez, Adalberto Gainaru, Ana Suter, Fred Markidis, Stefano Schlatter, Philipp Klaskyll, Scott Laure, Erwin Max Planck Computing and Data Facility Germany Oak Ridge National Laboratory United States KTH Royal Institute of Technology Sweden Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

ISBN: (纸本)9798331524937

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications. © 2025 IEEE.

关键词： data visualization

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Automated Extraction of Spatio-Semantic Graphs for Identifying Cognitive Impairment

Automated Extraction of Spatio-Semantic Graphs for Identifyi...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Si-Ioi Ng Pranav S. Ambadi Kimberly D. Mueller Julie Liss Visar Berisha Arizona State University USA University of Wisconsin-Madison USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing methods for analyzing linguistic content from picture descriptions for assessment of cognitive-linguistic impairment often overlook the participant’s visual narrative path, which typically requires eye tracking to assess. Spatio-semantic graphs are a useful tool for analyzing this narrative path from transcripts alone, however they are limited by the need for manual tagging of content information units (CIUs). In this paper, we propose an automated approach for estimation of spatio-semantic graphs (via automated extraction of CIUs) from the Cookie Theft picture commonly used in cognitive-linguistic analyses. The method enables the automatic characterization of the visual semantic path during picture description. Experiments demonstrate that the automatic spatio-semantic graphs effectively differentiate between cognitively impaired and unimpaired speakers. Statistical analyses reveal that the features derived by the automated method produce comparable results to the manual method, with even greater group differences between clinical groups of interest. These results highlight the potential of the automated approach for extracting spatio-semantic features in developing clinical speech models for cognitive impairment assessment.

关键词： visualization Dictionaries Statistical analysis Semantics Manuals Tagging Signal processing Feature extraction data mining Speech processing

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Pre-training with Synthetic Patterns for Audio

Pre-training with Synthetic Patterns for Audio

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuchi Ishikawa Tatsuya Komatsu Yoshimitsu Aoki LY Corporation Keio University Tokyo Japan LY Corporation Tokyo Japan Keio University Kanagawa Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose to pre-train audio encoders using synthetic patterns instead of real audio data. Our proposed framework consists of two key elements. The first one is Masked Autoencoder (MAE), a self-supervised learning framework that learns from reconstructing data from randomly masked counterparts. MAEs tend to focus on low-level information such as visual patterns and regularities within data. Therefore, it is unimportant what is portrayed in the input, whether it be images, audio mel-spectrograms, or even synthetic patterns. This leads to the second key element, which is synthetic data. Synthetic data, unlike real audio, is free from privacy and licensing infringement issues. By combining MAEs and synthetic patterns, our framework enables the model to learn generalized feature representations without real data, while addressing the issues related to real audio. To evaluate the efficacy of our framework, we conduct extensive experiments across a total of 13 audio tasks and 17 synthetic datasets. The experiments provide insights into which types of synthetic patterns are effective for audio. Our results demonstrate that our framework achieves performance comparable to models pre-trained on AudioSet-2M and partially outperforms image-based pre-training methods.

关键词： data privacy visualization Autoencoders Self-supervised learning Signal processing Licenses data models Speech processing Image reconstruction Synthetic data

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Case Study on the Effectiveness Feature Engineering Techniques for Performance Improvement of data Mining Applications

Case Study on the Effectiveness Feature Engineering Techniqu...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： Shruti Mehra B S Shajee Mohan Neha Agarwal M.Tech CSE Data Science Sharda University Gr.Noida Sharda University Gr.Noida

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

In this paper we try to demonstrate the effectiveness of different techniques for feature selection and optimization strategies that are used for data mining applications. Feature extraction is an important step in every data mining application, which aims to find discriminative patterns, interconnection, and structures within large datasets. In this paper we try to illustrate the effective use of the classical methods such as Principal Component Analysis (PCA), Kernel principal component analysis (KPCA), as well as more innovatory methods to better represent the data so as to achieve a better performance of the underlying pattern analysis, datamining tasks. We also consider effective representation of the data items in a discriminative way. We demonstrate this by making use of synthetic data so that a visual analysis can be made. We also use 3 real world datasets to demonstrate the effectiveness of PCA and KPCA based representations for obtaining improved performance for an SVM based multi-class classifier.

关键词： Wavelet transforms Text mining Support vector machines visualization Feature extraction Transformers data mining Kernel Principal component analysis Synthetic data

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Mapping Network Paths: Enhancing Traceroute Testing for Network Analysis

Mapping Network Paths: Enhancing Traceroute Testing for Netw...

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international conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Hasan Ahammad Md. Samsuzzaman Faculty of Computer Science & Engineering Patuakhali Science & Technology University Patuakhali Bangladesh Department of Computer & Communication Engineering Patuakhali Science & Technology University Patuakhali Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

As the world and its technologies continue to develop, complicating processes of connecting multiple networks, network management and engineering professionals need to have the skills to diagnose network connectivity problems. One such network analytical tool goes by the name of traceroute and its primary use is to trace the path that the packets take from the source host to the destination host in the given IP network. However, understanding of Traceroute results that are usually printed in the form of a text table would be hardly possible for a common user and in any case could not give a sufficient idea of the network structure. This challenge is lack of visual tool to analyze the traceroute result in a graphical manner, The Traceroute visualization Tool is the solution for this challenge because it offers the means for visualizing the traceroute results with an interface that is graphically friendly and allows for an interactive map on which to display such results. As previously explained, the packet route can be visualized simplistically using geocoding techniques which allow the conversion of IP addresses to geographical coordinates. Through the use of network hops, latency and many other features, it becomes easier for users to recognize various problems that may be associated with networks and badly connection points hence making it easier for users to diagnose problematic points in their networks.

关键词： visualization data visualization Network analyzers Reliability engineering Real-time systems Teamwork IP networks Usability Optimization Testing

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