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Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule 30

Effective Integration of Text Diffusion and Pre-Trained Lang...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ou, Yimin Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Diffusion models have become a powerful generative modeling paradigm, achieving great success in continuous data patterns. However, the discrete nature of text data results in compatibility issues between continuous diffusion models (CDMs) and pre-trained language models (PLMs). That is, the performance of diffusion models even degrades when combined with PLMs. To alleviate this issue, we propose to utilize a pre-trained decoder to convert the denoised embedding vectors into natural language instead of using the widely used rounding operation. In this way, CDMs can be more effectively combined with PLMs. Additionally, considering that existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation, we propose a linguistic easy-first schedule that incorporates the measure of word importance, conforming to easy-first-generation linguistic features and bringing about improved generation quality. Experiment results on the E2E dataset and five controllable tasks show that our approach can combine the merits of CDMs and PLMs, significantly outperforming other diffusion-based models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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Intrusion Detection Systems to avoid Cyberattack using Machine Learning 7

Intrusion Detection Systems to avoid Cyberattack using Machi...

引用

7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Paithankar, Om Nandkumar Haider, Armaan Mohammed Suman, Preetam Padhy, Sasmita Siddiqui, Masood H Dash, Sachikanta Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Bhopal India University of Lucknow Department of Statistics Uttar Pradesh Lucknow India Giet University Department of Cse Odisha Gunupur India

ISBN: (纸本)9798350350067

Machine learning brings another perspective into intrusion detection systems. Conventional techniques stumble at the pace of fast-evolving cyber threats, so we delve into machine learning-based intrusion detection systems. In this paper, we survey different datasets using different models: supervised, unsupervised, and hybrid. The work presents feature selection, model optimization, and performance metrics. This paper handles well-known challenges connected with imbalanced data and risks of overfitting. Ensembling and deep learning realize stunning improvements over the traditional approaches. These findings offer in-depth views of practical ways in which machine learning can be integrated into network security with a clear way forward toward real adaptive resilient intrusion detection, underpinning the transformative role of ML in hardening defenses within the cyber domain against sophisticated attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Fall Detection using LiDAR Sensor with PointNet++

Fall Detection using LiDAR Sensor with PointNet++

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Anish Sai Ayyagari Syed Ashfaq Ahmed Shri Venkatakrishnan Senthil Kumar Thangavel Jeyakumar G Madhusudana Rao Nalluri Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In today's world, fall incidents pose a significant risk to the elderly, often resulting in severe consequences. Falls can cause damage to the body, especially in elders, where it can be *** is vital to detect falls in order to provide assistance. The simple solution is to install cameras in required areas, which would constantly monitor the area for any falls occurring, this poses a privacy issue in high-risk regions such as bathrooms and toilets. Keeping this in mind, this paper proposes the use of LiDAR sensors that do not cause any invasion of privacy for fall detection. The paper further uses the Pointnet++ to detect fall from the LiDAR scans. The model achieves an accuracy of 85.1 % and precision of 84.8 % on a 70–30 split of the dataset and an accuracy of 86.3 % and precision of 86.2 % on an 80–20 split of the dataset.

关键词： Deep learning Privacy Sentiment analysis Laser radar Accuracy Cameras Sensors Fall detection Older adults Monitoring

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Righteous: Automatic Right-Sizing for Complex Edge Deployments

Righteous: Automatic Right-Sizing for Complex Edge Deploymen...

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IEEE/ACM Symposium on Edge computing (SEC)

作者： Aniruddha Rakshit Salil Reddy Rajiv Ramnath Anish Arora Jayson Boubin School of Computing Binghamton University Department of Computer Science and Engineering Ohio State University

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

Edge deployments perform complex deep learning inference and analysis in the wild in highly resource constrained environment. They are positioned everywhere from our largest cities to the bottom of our oceans, and often necessitate significant financial resources and labor to create and deploy. These properties make correctness of edge deployments simultaneously extremely important and difficult to verify a priori. In the past decade, a series of IoT and cloud testbeds have emerged to facilitate this testing. They provide users with access to resources and, less often, sensors that can be used to emulate workloads before deployment. While developers can use these resources to verify the correctness of their configurations, often users would like to “right-size” their deployments - that is, to find a minimal resource configuration that guarantees correctness - to decrease cost and prevent over-provisioning. The current suite of cloud and IoT testbeds does not provide this capability. We present Righteous, an automatic deployment right-sizing tool for edge deployments. Righteous treats configuration as a hyperparameter optimization problem, testing hyperparameter combinations to find a near-optimal configuration as quickly as possible. Righteous uses a new optimization algorithm, informed Pareto Simulated Annealing (iPSA) to find near-optimal configurations faster than other leading approaches. We use Righteous in conjunction with the PROWESS testbed to optimize a drone swarm deployment workload. Our results demonstrate that Righteous configurations use up to 3.5X less resources than those identified by leading hyperparameter tuning and resource allocation techniques, and does so up to 76.3X faster.

关键词： Software algorithms Urban areas Simulated annealing Hyperparameter optimization Software Sensors Resource management Optimization Tuning Testing

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Empowering Collaborative Filtering Using Probabilistic Diffusion Models

Empowering Collaborative Filtering Using Probabilistic Diffu...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Yu Hou Won-Yong Shin School of Mathematics and Computing (Computational Science and Engineering) Yonsei University Seoul Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Probabilistic diffusion models, a type of deep gen-erative models, have become one of emerging topics thanks to their state-of-the-art performance in computer vision and natural language processing (NLP) domains. Due to their denoising nature, diffusion models align well with recommender systems, where noisy user-item historical interactions are given. Diffusion models can effectively recover the original interactions from corrupted ones. Nevertheless, there are various challenges in applying diffusion models to recommender systems. To under-stand the challenges and solutions, there should be a reference for researchers and practitioners working on diffusion-based recommender systems. To this end, we first provide a comprehen-sive overview of diffusion-based collaborative filtering techniques, covering both 1) standard (non-sequential) recommendation and 2) sequential recommendation. We first explain the fundamental concepts of collaborative filtering and probabilistic diffusion models, and then summarize their applications to both standard and sequential recommendation settings.

关键词： Reviews Collaborative filtering Noise reduction Diffusion models Probabilistic logic Information filters Natural language processing Noise measurement Recommender systems Standards

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Real Estate Price Prediction Model Analysis Using Machine Learning 1

Real Estate Price Prediction Model Analysis Using Machine Le...

引用

1st International Conference on computing, sciences and Communications, ICCSC 2024

作者： Rajput, Kapil Bahadure, Nilesh Bhaskarrao Mishra, Amit Kumar Diwakar, Manoj Lamba, Sumita Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Pune India Graphic Era Deemed to be University Department of CSE Dehradun India School of Computing DIT University India

ISBN: (纸本)9798350353648

The research illustrates use of ML algos in the field of housing price prediction. The models have been analyzed on real datasets downloaded from Kaggle created by Amitabha Chakraborty. We know that the source on the research of ML prediction in India are scarce, but we encounter conditions which pose need of efficient methodologies to solve this problem. This research gives a glance about the use of ML techniques on the dataset. After careful analysis of data processed and running tests in multiple ways, the results indicate the best suited algorithms for this work. © 2024 IEEE.

关键词： Prediction models

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A Residual Network and Bi-directional LSTM based Hybrid Approach to Remote Sensing Image Captioning

A Residual Network and Bi-directional LSTM based Hybrid Appr...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Sree, M Ramya Siddhartha, Mettu Vardhan Reddy, Poli Vamsi Kruthika, B. Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Remote sensing imagery and captioning are invaluable when it comes to monitoring the Earth's surface for different objectives such as environmental effects, land usage and monitoring like agriculture, disaster relief, etc. Based on the residual convolutional neural networks, ResNet50 and bi-directional long-short term memory (Bi-LSTM) model, this work proposes a hybrid approach to accomplish remote sensing image captioning. The comparison of the proposed approach is analyzed against different state-of-art CNN models and LSTM networks for image spatial and spectral feature learning in remote sensing imagery using Bleu scores, Meteor and Rouge metrics. The study provides an understanding of which kind of and how these systems should be developed to facilitate better data analysis and usage to enhance the construction of elaborated remote sensing image captioning framework to support specific image interpretation for the prevention of environmental degradation, disasters, etc. The proposed ResNet50-BiLSTM model is the most effective in terms of achieving the highest BLEU-1 with 75.45%, BLEU-2 with 65.98%, BLEU-3 with 56.54%, BLEU-4 of 44.636%, ROUGE with 42.65% and METEOR with 40.69% scores, and higher generality than other models. © 2025 The Author(s).

关键词： Photointerpretation

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A Comprehensive Survey on Security of Single Source Cloud to Distributed Enviroments Edge and Fog computing 1

引用

International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Yadav, Dushyant Kumar Sahu, Hemlal Saradhi, Tejendra Dhanvinesh, K. Vishnubalaji, Loganatha Rana, Saurabh Chaudhary, Dharminder Department of Mathematics J.Y. Govt. College Raipur India Bennett University Noida India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9789819973835

ISBN: (纸本)9789819973828

The study of information security and privacy is currently quite popular. In parallel, several computing paradigms, such as cloud and edge computing, are already creating a unique ecosystem with various designs, storage possibilities, and processing capacities. The diversity of this ecosystem has several limitations, particularly in terms of security and privacy concerns. In the numerous paradigms discussed, this systematic literature review highlights overlaps, distinctions, direct assaults, and defences. The primary conclusion identifies the most critical security and privacy risks. The results highlight significant parallels and divergences between the cloud, edge, and fog computing models. The research also revealed that this ecosystem’s heterogeneity has problems and significantly hinders the adoption of security and privacy procedures from defending against security breaches and privacy leaks. The review investigations discovered many deployment strategies to improve safety and privacy flaws. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Big Data Application in Cancer Classification by Analysis of RNA-seq Gene Expression

Big Data Application in Cancer Classification by Analysis of...

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Intelligent Technologies (CONIT), International Conference on

作者： Amruth A Ramanan R Rhea Paul Vishal S S Saravanan Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India Dept. of Computer Science and Engineering Amrita School of Computing Chennai India

Computer science domain has got massive developments in the past decade, especially in the field of Big Data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and services. Healthcare is one such industry which comes across voluminous data. Big Data platform is used to process the data and subsequently AI models can be used for prediction or classification. This paper focuses on proposing a Spark based pipeline to reduce the time required to process the voluminous data. With the aid of this new pipeline, we classify the type of cancer through analysis of RNA-seq gene expression levels. The Spark-based pipeline is run on default standalone mode and on a cluster. The study recorded that the Spark-based pipeline run on cluster mode improved the performance approximately by 11 times (regarding processing costs) when compared to the performance of Python-based pipeline.

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