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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoli Tang Han Yu College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Auction-based Federated Learning (AFL) has become a significant focus in recent years. Existing approaches for model users (MUs) in FL generally operate under the assumption that the entire set of essential data owners (DOs) must be gathered before the training process initiates. However, in practical scenarios, the MU can initiate the FL training process multiple times, gradually recruiting DOs across multiple FL training sessions. Current AFL methods are not equipped to handle such situations. The challenge of optimizing the AFL budget across multiple sessions and objectives remains unresolved. To address this gap, we propose the Efficient and Utility Return-Optimizing budget management strategy for MUs in Auction-based Federated Learning (EURO-AFL). By incorporating the hierarchical reinforcement learning framework, EURO-AFL concurrently optimizes inter-session budget pacing and intra-session budget allocation, with the dual objective of maximizing total utility while minimizing waiting time. Extensive experiments conducted on four real-world datasets illustrate the substantial advantages of EURO-AFL compared to five state-of-the-art baselines. It outperforms the best-performing baseline by 6.26%, 47.9% and 17.6% in terms of model accuracy, number of training sessions sustained, and training efficiency, respectively. To our best knowledge, EURO-AFL is the first multi-session multi-objective budget optimization approach designed for AFL MUs.

关键词： Training Knowledge engineering Accuracy Federated learning Neural networks Reinforcement learning data models

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Strategic Optimization of Blood Supply Chain Management for Efficient Blood Bank Operations 3

Strategic Optimization of Blood Supply Chain Management for ...

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3rd AIxIA Workshop on Artificial Intelligence For Healthcare, HC@AIxIA 2024

作者： Ndlalane, Simmera Otegbeye, Olumuyiwa Ezugwu, Absalom E. School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street North-West Potchefstroom2520 South Africa

South Africa faces a critical shortage of blood donors, leading to substantial deficits in the national blood supply. Blood donations are vital for the treatment of life-threatening conditions, making it crucial to develop efficient models for the management of blood stocks. This paper presents a mathematical model to optimize blood donation and ensure sufficient supply to meet fluctuating demands. The model captures the complex interactions within the blood banking system, focusing on minimizing costs, reducing waste, and efficiently distributing blood units. Specifically, it addresses daily supply challenges by minimizing the need for emergency imports and reducing blood wastage due to expiration while meeting all demand requirements. The core objective is to minimize blood wastage and reduce the reliance on imported blood banks during emergencies. The proposed objective function incorporates variables such as emergency importation and expiration rates, and robust optimization techniques are applied to identify optimal solutions while satisfying operational constraints. Symbiotic Organism Search (SOS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods are utilized for optimization. Among these, SOS demonstrated superior performance, achieving the lowest levels of importation and wastage. However, the algorithms were unable to significantly reduce supply levels due to the accumulation of excess stock from the previous day, which carried over into the next day. This paper provides valuable information on blood supply management and highlights the potential for optimization techniques to improve efficiency and sustainability in blood banking. © 2024 CEUR-WS. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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14th International Conference on Cloud computing, data science and Engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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SparX: A Sparse Cross-Layer Connection Mechanism for Hierarchical Vision Mamba and Transformer Networks 39

SparX: A Sparse Cross-Layer Connection Mechanism for Hierarc...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lou, Meng Fu, Yunxiang Yu, Yizhou School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Due to the capability of dynamic state space models (SSMs) in capturing long-range dependencies with linear-time computational complexity, Mamba has shown notable performance in NLP tasks. This has inspired the rapid development of Mamba-based vision models, resulting in promising results in visual recognition tasks. However, such models are not capable of distilling features across layers through feature aggregation, interaction, and selection. Moreover, existing cross-layer feature aggregation methods designed for CNNs or ViTs are not practical in Mamba-based models due to high computational costs. Therefore, this paper aims to introduce an efficient cross-layer feature aggregation mechanism for vision backbone networks. Inspired by the Retinal Ganglion Cells (RGCs) in the human visual system, we propose a new sparse cross-layer connection mechanism termed SparX to effectively improve cross-layer feature interaction and reuse. Specifically, we build two different types of network layers: ganglion layers and normal layers. The former has higher connectivity and complexity, enabling multi-layer feature aggregation and interaction in an input-dependent manner. In contrast, the latter has lower connectivity and complexity. By interleaving these two types of layers, we design a new family of vision backbone networks with sparsely cross-connected layers, achieving an excellent trade-off among model size, computational cost, memory cost, and accuracy in comparison to its counterparts. For instance, with fewer parameters, SparX-Mamba-T improves the top-1 accuracy of VMamba-T from 82.5% to 83.5%, while SparX-Swin-T achieves a 1.3% increase in top-1 accuracy compared to Swin-T. Extensive experimental results demonstrate that our new connection mechanism possesses both superior performance and generalization capabilities on various vision tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： State space methods

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Sustainability Forecasting for Deep Learning Packages 31

Sustainability Forecasting for Deep Learning Packages

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Han, Junxiao Wang, Yunkun Liu, Zhongxin Bao, Lingfeng Liu, Jiakun Lo, David Deng, Shuiguang School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798350330663

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as 'sus-tainable' or 'dormant'. Relatedly, a DL package is considered as 'sustainable' if it has sustained activity in its last 12 months. Otherwise, it is deemed as 'dormant'. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers. ©

关键词： Open source software

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An Investigation on the Potential of KAN in Speech Enhancement

arXiv

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arXiv 2024年

作者： Li, Haoyang Hu, Yuchen Chen, Chen Chng, Eng Siong Nanyang Technological University College of Computing and Data Science Singapore

High-fidelity speech enhancement often requires sophisticated modeling to capture intricate, multiscale patterns. Standard activation functions, while introducing nonlinearity, lack the flexibility to fully address this complexity. Kolmogorov-Arnold Networks (KAN), an emerging methodology that employs learnable activation functions on graph edges, present a promising alternative. This work investigates two novel KAN variants based on rational and radial basis functions for speech enhancement. We integrate the rational variant into the 1D CNN blocks of Demucs and the GRU-Transformer blocks of MP-SENet, while the radial variant is adapted to the 2D CNN-based decoders of MP-SENet. Experiments on the VoiceBank-DEMAND dataset show that replacing standard activations with KAN-based activations improves speech quality across both the time-domain and time-frequency domain methods with minimal impact on model size and FLOP, underscoring KAN's potential to improve speech enhancement models. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Machine Learning Methods for Breast Cancer Diagnosis

Machine Learning Methods for Breast Cancer Diagnosis

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Ye Xiaoyi Lee Sui Ping School of Computing and Data Science Xiamen University Malaysia Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

Breast cancer is an important issue in the field of women's health. Early detection and diagnosis are crucial to treatment and prognosis. However, traditional breast cancer detection methods have some limitations, and it is challenging to accurately identify tumors. Machine learning technology can play an important role in early diagnosis of breast cancer. This paper aims to develop an accurate and efficient machine learning model that can use clinical data to automatically detect breast cancer, so as to judge whether the tumor is malignant or benign. In this study, we explore the application of machine learning algorithms in the classification of breast cancer using the Wisconsin Breast Cancer database. A machine learning framework is proposed. Leveraging the rich clinical data provided by this database, we employ various classifiers, including KNN, Random Forest, SVM, Logistic Regression, and Naive Bayes, to develop AI algorithm for breast cancer detection. This paper implements and compare this machine learning algorithms based on their performance metrics, such as accuracy, precision, recall, and F-measure. Experimental results indicate that SVM outperforms other classifiers, achieving high precision, recall, and F-measure values for both malignant and benign samples. This result suggests that SVM is well-suited for breast cancer detection tasks and holds significant potential for clinical applications.

关键词： Support vector machines Technological innovation Machine learning algorithms Accuracy databases Nearest neighbor methods Breast cancer Prognostics and health management Random forests Tumors

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Diffusion Model-Based Video Editing: A Survey

arXiv

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arXiv 2024年

作者： Sun, Wenhao Tu, Rong-Cheng Liao, Jingyi Tao, Dacheng The College of Computing and Data Science Nanyang Technological University Singapore

The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making"what you want is what you see" a reality. Among these, video editing has gained substantial attention and seen a swift rise in research activity, necessitating a comprehensive and systematic review of the existing literature. This paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. We begin by overviewing the mathematical formulation and image domain’s key methods. Subsequently, we categorize video editing approaches by the inherent connections of their core technologies, depicting evolutionary trajectory. This paper also dives into novel applications, including point-based editing and pose-guided human video editing. Additionally, we present a comprehensive comparison using our newly introduced V2VBench. Building on the progress achieved to date, the paper concludes with ongoing challenges and potential directions for future research. A project related to this paper can be found at https://***/wenhao728/awesome-diffusion-v2v. © 2024, CC BY.

关键词： Video signal processing

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Evaluating and Improving Optical Character Recognition (OCR) Efficiency in Recognizing Mandarin Phrases with Phonetic Symbols

Evaluating and Improving Optical Character Recognition (OCR)...

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2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022

作者： Lo, Shi Wei Chou, Hsiu-Mei National Center for High-Performance Computing Data Science and Technology Division Hsinchu Taiwan

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

The development of tools and materials for those people who are visually impaired is a crucial topic in the research area of assistive technologies. In mandarin learning, most printing materials for K-4 children have phonetic symbols to assist students in learning pronunciation. One challenge in the research area is recognizing the mandarin phrases in those printing materials and transforming them into audiobooks or Braille. To our knowledge, this is the first study examining the Optical Character Recognition (OCR) performance toward phonetic symbols in mandarin. In this study, we conduct experiments on recognizing images with mandarin phrases with phonetic symbols by the side using the OCR system. We propose candidate methods to improve recognition efficiency in the future based on preliminary results. © 2022 IEEE.

关键词： Optical character recognition

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