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The Importance of Robust Features in Mitigating Catastrophic Forgetting 28

The Importance of Robust Features in Mitigating Catastrophic...

引用

28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Khan, Hikmat Bouaynaya, Nidhal C. Rasool, Ghulam Rowan University Dept. of Electrical and Computer Engineering GlassboroNJ United States Moffitt Cancer Center Dept. of Machine Learning TampaFL United States

ISBN: (纸本)9798350300482

Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or data distributions. The adversarial robustness has decomposed features into robust and non-robust types and demonstrated that models trained on robust features significantly enhance adversarial robustness. However, no study has been conducted on the efficacy of robust features from the lens of the CL model in mitigating catastrophic forgetting in CL. In this paper, we introduce the CL robust dataset and train four baseline models on both the standard and CL robust datasets. Our results demonstrate that the CL models trained on the CL robust dataset experienced less catastrophic forgetting of the previously learned tasks than when trained on the standard dataset. Our observations highlight the significance of the features provided to the underlying CL models, showing that CL robust features can alleviate catastrophic forgetting. © 2023 IEEE.

关键词： learning systems

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Edge Computing Devices Authentication using Quantum Computing 15

Edge Computing Devices Authentication using Quantum Computin...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Telsang, Vinayak A. Kakkasageri, Mahabaleshwar S. Devangavi, Anil D. Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (纸本)9798350370249

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(VV), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices. ©2024 IEEE.

关键词： Authentication Blockchain Cryptography Edge computing Quantum key

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A new multi-source light detection benchmark and semi-supervised focal light detection 24

A new multi-source light detection benchmark and semi-superv...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jae-Yong Baek Yong-Sang Yoo Seung-Hwan Bae Inha University Dept. of Electrical and Computer Engineering Vision and Learning Lab.

ISBN: (纸本)9798331314385

This paper addresses a multi-source light detection (LD) problem from vehicles, traffic signals, and streetlights under driving scenarios. Albeit it is crucial for autonomous driving and night vision, this problem has not been yet focused on as much as other object detection (OD). One of the main reasons is the absence of a public available LD benchmark dataset. Therefore, we construct a new large LD dataset consisting of different light sources via heavy annotation:YouTube Driving Light Detection dataset (YDLD). Compared to the existing LD datasets, our dataset has much more images and box annotations for multi-source lights. We also provide rigorous statistical analysis and transfer learning comparison of other well-known detection benchmark datasets to prove the generality of our *** the recent object detectors, we achieve the extensive comparison results on YDLD. However, they tend to yield the low mAP scores due to the intrinsic challenges of LD caused by very tiny size and similar appearance. To resolve those, we design a novel lightness focal loss which penalizes miss-classified samples more and a lightness spatial attention prior by reflecting a global scene context. In addition, we develop a semi-supervised focal light detection (SS-FLD) by embedding our lightness focal loss into the semi-supervised object detection (SSOD). We prove that our methods can consistently boost mAP to the variety of types of recent detectors on YDLD. We will open both YDLD and SS-FLD code at https://***/YDLD-dataset/YDLD.

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Natural Language Processing based Automated Essay Scoring with Parameter-Efficient Transformer Approach 6

Natural Language Processing based Automated Essay Scoring wi...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Sethi, Angad Singh, Kavinder Delhi Technological University Machine Learning Research Lab Dept. of Computer Science Delhi India

ISBN: (纸本)9781665410281

Existing automated scoring models implement layers of traditional recurrent neural networks to achieve reasonable performance. However, the models provide limited performance due to the limited capacity to encode long-term dependencies. The paper proposed a novel architecture incorporating pioneering language models of the natural language processing community. We leverage pre-trained language models and integrate it with adapter modules, which use a bottle-neck architecture to reduce the number of trainable parameters while delivering excellent performance. We also propose a model by re-purposing the bidirectional attention flow model to detect adversarial essays. The model we put forward achieves state-of-the-art performance on most essay prompts in the Automated Student Assessment Prize data set. We outline the previous methods employed to attempt this task, and show how our model outperforms them. © 2022 IEEE.

关键词： Bottles

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Unveiling Customer Segmentation Patterns in Credit Card Data using K-Means Clustering: A machine learning Approach

Unveiling Customer Segmentation Patterns in Credit Card Data...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Priyadarshni, Sakshi Fathima, Rakshan Urolagin, Siddhaling Bongale, Anupkumar M. Dharrao, Deepak Sudhakar Birla Institute of Technology and Science Dept. of Computer Science Pilani Dubai United Arab Emirates Symbiosis Institute of Technology Dept. of Artificial Intelligence and Machine Learning Pune India Symbiosis Institute of Technology Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350393415

Customer segmentation plays a crucial role in business development by providing deep insights into customer behaviors and preferences, facilitating effective and personalized marketing strategies. However, traditional segmentation methods often fall short of accurately analyzing large and complex datasets. In recent years, the application of machine learning techniques has transformed the field of customer segmentation, enabling automated analysis, enhanced accuracy, and the discovery of intricate patterns and key trends. This research paper provides a detailed review of the application of K-Means clustering in credit card companies for effective customer segmentation. It explores the benefits, challenges, and practical considerations of utilizing K-Means clustering, along with real-world case studies highlighting trends and potential advancements for using K-Means clustering in credit card customer segmentation. © 2023 IEEE.

关键词： K-means clustering

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Rotation-Invariant Self-Supervised Representation learning for Object Detection in Autonomous Driving 2

Rotation-Invariant Self-Supervised Representation Learning f...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Marathe, Aboli Roy, Sidharth Singh, Devansh Walambe, Rahee Kotecha, Ketan Machine Learning Dept. Carnegie Mellon University Pittsburgh United States Dept. of Computer and Information Science University of Pennsylvania Philadelphia United States Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (纸本)9798331504106

This paper describes a self supervised representation learning approach that can perform robust object detection in out-of-distribution rotated images for autonomous driving task. Keeping in mind the limitations of vehicle sensors we incorporate self-supervised rotation invariance for correcting rotational corruptions. We present a comprehensive analysis of observed label shifts, validate the results using holdout sets and provide explainability for the performance uisng GradCAM appaorach. We demonstrate results on real-world adversarial examples: (Autonomous Scenes - DAWN dataset) that significantly outperform common methods. The proposed framework outperforms the rotation-affected benchmark by 14.73 points of mAP for the OD task in autonomous driving applications. ©2024 IEEE.

关键词： Self-supervised learning

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Biomedical Interventions in Precision Medicine for Diabetics 3

Biomedical Interventions in Precision Medicine for Diabetics

引用

3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Madhumitha, A. Dhananjayan, V. Bannari Amman Institute of Technology Dept. of Computer Science and Bussiness System Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Dept. of Artificial Intelligence and Machine Learning Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350370867

Precision medicine is the need of the hour strategy that deals with precision from diagnosis to treatment. There is a growing need for continuous monitoring of blood glucose levels and an effective algorithm to determine the right insulin dosages to achieve precision medicine for diabetes. This work intends to review the impact and importance of biomedical interventions in precision medicine for diabetes. It addresses the barriers that persist in practicing precision medicine for diabetes by strengthening the techniques of precision diagnosis and prognosis, precision therapeutics, precision treatments, and precision monitoring using predictive algorithms. The review emphasizes the significance of biomedical interventions in advancing the future directions of precision medicine for diabetes, including the integration of clinical trials with large datasets and continuous evaluation and monitoring of patients through regular measurements of blood glucose levels. By focusing on these biomedical interventions, this review aims to provide insights into the role of precision medicine in diabetes and its potential for improving patient outcomes. © 2023 IEEE.

关键词： Diagnosis

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Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sivakumar, S. Dharani, U. Hephzibah, Dineep Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tiruchirapalli India Srm Institute Of Science And Technology Dept. Of Comp. Sci. And Engineering Specialization In Artificial Intelligence And Machine Learning Tiruchirapalli India

ISBN: (纸本)9798350352689

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture. © 2024 IEEE.

关键词： Soil testing

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Habit Coach: Customising RAG-based chatbots to support behavior change 3

Habit Coach: Customising RAG-based chatbots to support behav...

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3rd Workshop on Artificial Intelligence for Human-machine Interaction, AIxHMI 2024

作者： Arabi, Arian Fooroogh Mand Koyuturk, Cansu O'Mahony, Michael Calati, Raffaella Ognibene, Dimitri Dept. Psychology Universita degli Studi di Milano Bicocca Milan Italy School of Computer Science Technological University Dublin Ireland SFI Centre for Research Training in Machine Learning Technological University Dublin Ireland

This paper presents the iterative development of Habit Coach, a GPT-based chatbot designed to support users in habit change through personalized interaction. Employing a user-centered design approach, we developed the chatbot using a Retrieval-Augmented Generation (RAG) system, which enables behavior personalization without retraining the underlying language model (GPT-4). The system leverages document retrieval and specialized prompts to tailor interactions, drawing from Cognitive Behavioral Therapy (CBT) and narrative therapy techniques. A key challenge in the development process was the difficulty of translating declarative knowledge into effective interaction behaviors. In the initial phase, the chatbot was provided with declarative knowledge about CBT via reference textbooks and high-level conversational goals. However, this approach resulted in imprecise and inefficient behavior, as the GPT model struggled to convert static information into dynamic and contextually appropriate interactions. This highlighted the limitations of relying solely on declarative knowledge to guide chatbot behavior, particularly in nuanced, therapeutic conversations. Over four iterations, we addressed this issue by gradually transitioning towards procedural knowledge, refining the chatbot's interaction strategies and improving its overall effectiveness. In the final evaluation, 5 participants engaged with the chatbot over five consecutive days, receiving individualized CBT interventions. The Self-Report Habit Index (SRHI) was used to measure habit strength before and after the intervention, revealing a reduction in habit strength post-intervention. These results underscore the importance of procedural knowledge in driving effective, personalized behavior change support in RAG-based systems. © 2024 for this paper by its authors.

关键词： User centered design

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