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Radar style transfer for metric robot localisation on lidar maps

CAAI Transactions on Intelligence Technology 2023年第1期8卷 139-148页

作者： Huan Yin Yue Wang Jun Wu Rong Xiong College of Control Science and Engineering Zhejiang UniversityHangzhouChina Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyKowloonHong KongChina

Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this paper,a cross-modality radar localisation on prior lidar maps is ***,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network *** with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar *** whole online localisation system only needs a rotating radar sensor and a pre-built global lidar *** the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car *** promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.

关键词： lidar local maps

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EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays 1

EssayDetect at GenAI Detection Task 2: Guardians of Academic...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Jayant, Subhashi Kumar, Saurabh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we propose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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Review of Machine Learning-Based Distributed Denial-of-Service (DDoS) Detection and Prevention 2nd

Review of Machine Learning-Based Distributed Denial-of-Serv...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Fahim, M.D. Shafayat Hossain, Md. Rosni, Tanjela Rahman Khadija, Sihama Ramjana Hasan, Mahamudul Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9789819776023

A distributed denial-of-service (DDoS) attack is a deliberate attempt to stop a target system, service, or network from operating normally by flooding it with an avalanche of unauthorized traffic or requests. In a DDoS assault, numerous hijacked computers or devices—often forming a botnet—are organized to simultaneously send the target an excessive amount of traffic or requests, overwhelming it and making it unable to react to valid user requests. DDoS attacks, such as volumetric attacks, TCP/IP protocol attacks, application layer attacks, and reflective/amplified attacks, can take many forms and target distinct weaknesses in the target infrastructure. DDoS attack prevention might be difficult, but there are different ways of detecting a DDoS attack. This paper focuses on finding and reviewing different kinds of ML-based DDoS detection techniques suggested in different papers and journals and comparing these methods and finding the best alternate solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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Movie Recommendation Using Web Crawling 2nd

Movie Recommendation Using Web Crawling

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Raj, Pronit Kumar, Chandrashekhar Shekhar, Harshit Kumar, Amit Paul, Kritibas Jana, Debasish Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In today’s digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real-time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content-based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real-time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data reduction

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Towards Safer Social Spaces: LSTM, Bi-LSTM and Hybrid Approach for Cyberbullying Detection in Assamese language on Social Networks 2

Towards Safer Social Spaces: LSTM, Bi-LSTM and Hybrid Approa...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Dutta, Surajit Neog, Mandira Baruah, Nomi Dibrugarh University Department of Computer Science & Engineering Dibrugarh India

ISBN: (纸本)9798350328202

Social media provides a forum for constructive discussion while also providing an environment that is conducive to cyberbullying. Cyberstalking is a growing problem, especially on social media sites like Facebook. It is defined as harassment that occurs online without any physical contact. To mitigate its negative effects, automated procedures for locating and removing cyberbullying content must be put in place. The intent of this research is to broaden the scope of cyberbullying detection, with an emphasis on Assamese. Due to the limited digital resources, Assamese has not received much consideration in previous research. The primary goal of this research is to fill the notable gap in the identification of cyberbullying, specifically in populations who are linguistically marginalised, by focusing on Assamese. Research findings show that LSTM obtained an accuracy of 87.86%, Bi-LSTM attained 89.58% and the hybrid model, LSTM and Bi-LSTM, surpass all with a remarkable accuracy of 89.7%. The research methodology introduces an innovative hybrid approach that melds Long Short-Term Memory (LSTM) models with Bidirectional LSTM (Bi-LSTM), specifically designed to accommodate the unique linguistic characteristics and challenges of Assamese. The results of the research demonstrate the hybrid model's efficacy in identifying cyberbullying in Assamese, indicating its potential utility in improving security when surfing the internet. © 2024 IEEE.

关键词： Long short-term memory

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Optimizing Menstruation Symptom Management: A Machine Learning Approach to Personalized Nutrition Recommendations

Optimizing Menstruation Symptom Management: A Machine Learni...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Logapriya, E. Surendran, R. Sathish Kumar, J.P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

This research provides through method for building a recommendation system for health concerns for women the significant number of individuals' day-to-day activities are affected by menstruation symptoms including pains, mood swings and fatigue. Be it making an individual food prescription or eradicating these symptoms, nutrition is an absolute factor. Thus, according to this work, there is a concept of a method that based on machine learning will help predict menstrual symptoms and provide tailored nutrition recommendations. The analysis of the respondents scores for six menstrual cycles revealed that magnesium, omega-3 fatty acids, vitamin D, and calcium are significant nutrients affecting the symptom severity level. Various Machine Learning models to compared include the Neural Networks, Support Vector Machines and Random Forests. Out of all the models the highest was achieved by the Random Forest model for the targeted recommendations of the proper nutrition adjusted to the person, where the prediction accuracy was 98%. The findings of the provided results support the view that is hope and the intensity of menstruation symptoms reduced significantly. © 2024 IEEE.

关键词： Support vector machines

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Uncertainty quantification in large language models through convex hull analysis

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Discover Artificial Intelligence 2024年第1期4卷 1-14页

作者： Catak, Ferhat Ozgur Kuzlu, Murat Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Engineering Technology Old Dominion University NorfolkVA United States

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. The responses are transformed into high-dimensional embeddings via a BERT model and subsequently projected into a two-dimensional space using Principal Component Analysis (PCA), Isomap, Multidimensional Scaling (MDS). The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the embeddings and compute the convex hull for each selected cluster. The experimental results indicate that the uncertainty of the model for LLMs depends on the prompt complexity, the model, and the temperature setting. © The Author(s) 2024.

关键词： Embeddings

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IT-Tuning: Parameter Efficient Information Token Tuning for Language Model 9

IT-Tuning: Parameter Efficient Information Token Tuning for ...

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9th Workshop on Representation Learning for NLP, RepL4NLP 2024 at ACL 2024

作者： Kim, Jungu Kim, Hyeoncheol Department of of Computer Science and Engineering Korea University Korea Republic of

ISBN: (纸本)9798891761575

Recently, language models have demonstrated exceptional performance compared to their predecessors. In this context, attention mechanisms and pre-training significantly contribute to the enhanced performance of modern language models. Additionally, a continuously increasing number of parameters plays a crucial role in these advancements. However, an increase in the number of parameters significantly increases the GPU memory and training time required during fine-tuning of language models, this makes fine-tuning infeasible in environments with limited computing resources. Furthermore, after fine-tuning, the storage space required for deployment increases proportionally with the number of tasks, making it challenging to deploy devices with limited storage capacities. In this study, we propose IT-Tuning, a Parameter Efficient Fine-Tuning method that introduces a new concept called information tokens to address these issues. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images

Convolutional Neural Networks for Non-Invasive Diagnosis of ...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Chhillar, Vaibhav Anupriya Kant, Ansh Samdarshi, Virat Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350378092

Androgenetic alopecia, a common form of hair loss affecting both men and women, poses significant psychosocial and quality-of-life impacts. Current diagnostic methods, such as trichoscopy and biopsy, are often invasive, time-consuming, and subject to inter-observer variability. This research proposes a non-invasive approach to alopecia diagnosis using deep learning techniques applied to dermoscopic hair images. We develop a convolutional neural network (CNN) model tailored for automated detection and classification of androgenetic alopecia patterns from scalp image data. Through extensive experimentation on a curated dataset of dermoscopic hair images, we evaluate the performance of our CNN model against existing methods and explore interpretability techniques to enhance model transparency and trust. Saliency maps and feature attribution methods are employed to provide localized explanations and identify influential image regions for model predictions. Our findings demonstrate the potential of deep learning for accurate and scalable alopecia diagnosis, while emphasizing the importance of interpretability in clinical decision support. We discuss the implications, limitations, and future directions for integrating our approach into dermatological practice and telehealth applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Waste Reduction Through Real-Time Defect Detection in Garment Sewing Using Variants YOLOv7

Optimizing Waste Reduction Through Real-Time Defect Detectio...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Uddin, Md. Minhaz Akter Sarmin, Sanzeda Foysal, Sadi Mahmud Rahman, Sadia Risti, Nushara Tazrin Rahman, Md. Khalilur Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

In the era of computer vision to overcome challenges, the introduction of the YOLO model revolutionized real-time computer vision approaches. In the garment industry, the inception of products plays a significant role while increasing the processing time with a good accuracy rate is the big challenge here. A real-time garments defect detection approach using YOLOv7, YOLOv7x, and YOLOv7-w6 on a primary dataset is proposed with a good FPS rate and better accuracy. Maximum traditional garments inception approaches focused on end product defects while this model suggests detecting defects in the sewing phase so that the cost of the rejected end product can be optimized by detecting them before a product goes through all the phases. For this our research focuses on three subclasses of Seam, Stitch, and Hole related to sewing phase defects. To increase the detection rate, the hyperparameter tuning technique is applied to the YOLOv7 model. Three models are proposed based on pre-trained weights of YOLOv7, YOLOv7x, and YOLOv7-w6 to compare the accuracy and FPS rate in terms of implementation in real-world projects. © 2024 IEEE.

关键词： Garment industry

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