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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yixuan Li, Zhidong Wang, Yang Chen, Fang Fan, Xuhui Zhou, Feng School of Statistics and Data Science Southeast University China Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

ISBN: (纸本)157735897X

Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier’s architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning algorithms

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System

Development of Fast Prediction System of User Action for a F...

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

作者： N.R. Rajalakshmi L. Sharmila S. Prabu T. Veeramakali G. Arulkumaran Pranshu Chourasia Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India School of Computing and Information Technology REVA UNIVERSITY Bangalore Karnataka India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Management accounting Heuristic algorithms Electricity Prediction algorithms Dynamic programming Batteries Temperature control Particle swarm optimization Energy management systems Optimization

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A Novel Dense-Net Deep Neural Network Method for Classify Tuberculosis Stages Using Chest X-Ray Images with Enhanced Features

A Novel Dense-Net Deep Neural Network Method for Classify Tu...

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data, Computation and Communication (ICDCC), International Conference on

作者： Ebtasam Ahmad Siddiqui Rajneesh Kumar Patel Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Jaipur India School of Computing Science & Engineering VIT Bhopa University India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Although chest X-ray (CXR) imaging is critical for the detection of potentially lethal disorders, interpretation may arise because to similarities between distinct lung conditions. In response, researchers proposed CBAMWDnet, a new deep learning model for tuberculosis (TB) diagnosis based on computed tomography (CXR) images. To successfully collect geographical and contextual data, the model includes the Convolutional Block Attention Module (CBAM) into its architecture, which is based on the Wide Dense Net (WDnet). Pre-processing using an enhanced Gabor filter improves image quality prior to applying the model. We test the model using three well-known datasets: Shenzhen, Montgomery, and Indiana. The results indicate that the Shenzhen dataset performs the best. CBAMWDnet beats previous techniques by better classifying tuberculosis (TB) into normal, primary, latent, and active stages. This complex classification engine improves both accuracy and dependability.

关键词： Image quality Accuracy Sensitivity Tuberculosis Computational modeling Reliability engineering data models Gabor filters X-ray imaging Biomedical imaging

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LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

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

作者： Wu, Shaoqi Xie, Weixing Peng, Youhong Li, Jinwen Yao, Jiawei Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Taiwan

ISBN: (纸本)9798350368741

With the popularity of short video platforms, the number of single-view videos has increased significantly. Existing NeRF-based methods can reconstruct dynamic scenes in a single-view setting, but slow rendering speed and low rendering quality limit their practical applications. To address these challenges, we propose a fast single-view scenes reconstruction framework based on 3D Gaussian Splatting. Our method uses point clouds obtained with depth priors as the Gaussian initialization and introduces learnable parametric functions to model the time-dependent deformation of Gaussians. The explicit deformation modeling for Gaussians significantly reduces training and rendering time. Furthermore, to improve the rendering quality of challenging areas, we adopt an adaptive sampling strategy to densify Gaussians. For occlusion problems from single-view videos, we design a smooth loss function to restore the color of the occluded areas. Experimental results demonstrate that our method significantly reduces training time, enhances rendering quality, and accelerates rendering speed. Project page: https://***/LPGaussians. © 2025 IEEE.

关键词： 3D Gaussian Splatting Dynamic Scene Reconstruction Single-view Reconstruction

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Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel dataset

arXiv

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arXiv 2024年

作者： Khanna, Sandeep Chattopadhyay, Chiranjoy Kundu, Suman Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India School of Computing and Data Sciences FLAME University India

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

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CausalDiff: causality-inspired disentanglement via diffusion model for adversarial defense 24

CausalDiff: causality-inspired disentanglement via diffusion...

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

作者： Mingkun Zhang Keping Bi Wei Chen Quanrun Chen Jiafeng Guo Xueqi Cheng CAS Key Laboratory of AI Safety Institute of Computing Technology CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS School of Statistics University of International Business and Economics

ISBN: (纸本)9798331314385

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/CAS-AISafetyBasicResearchGroup/CausalDiff.

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Joint Compression and Deadline Optimization for Communication-Efficient Federated Edge Learning

Joint Compression and Deadline Optimization for Communicatio...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Zhang, Maojun Cai, Zhijie Liu, Dongzhu Jin, Richeng Zhu, Guangxu Zhong, Caijun Shenzhen Research Institute of Big Data Shenzhen China School of Computing Science University of Glasgow United Kingdom College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Guangzhou China

ISBN: (纸本)9798350370218

The federated edge learning (FEEL) framework is a popular approach for privacy-preserving collaborative model training, where edge devices and the server exchange learning updates frequently. In this vein, FEEL can cause a heavy burden on the communication system due to the limited bandwidth and severe interference between devices. Moreover, under the common FEEL framework, the server needs to wait for the update from the slowest device before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. To address these practical issues, we formulate a training time minimization problem that optimizes the compression ratio and deadline. To this end, the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is simplified and solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments show that the proposed method can greatly accelerate the vanilla FedAVG algorithm, and outperforms the state-of-the-art schemes. © 2023 IEEE.

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Digital Forensics Analysis of iMazing Phone Explorer Tool to Aid Mobile Forensics Investigation

Digital Forensics Analysis of iMazing Phone Explorer Tool to...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Ashar Neyaz Avinash Kumar Bingyu Liu School of Computing & Data Science Wentworth Institute of Technology Boston MA USA Department of Computer Science Sam Houston State University Huntsville TX USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Apple devices continue to lead the smart device industry, with over 2.2 billion active devices worldwide. These devices, including iPhones and iPads, store extensive personally identifiable information (PII), making them vital in digital forensic investigations. This study specifically examines the retrieval and analysis of PII from an iPhone and an iPad using iMazing software as the sole forensic tool. data acquisition was conducted via a Lightning-to-VSB-A connection without jailbreaking, focusing on two distinct scenarios-one for each device-restricting access to user-space data. The analysis uncovered a range of forensic artifacts, including iCloud-synced SSID data, message logs, Wi-Fi security protocols, and other critical digital traces. Extracted data appeared in multiple formats, such as text files, CSV files, and plist files, providing forensic investigators with valuable insights. While this study is limited to iMazing, its findings highlight the potential and limitations of third-party tools in mobile forensics. By leveraging iMazing's capabilities, this research contributes to the evolving field of mobile device forensics, reinforcing the role of Apple devices in enhancing digital investigative methodologies.

关键词： data privacy Protocols Digital forensics Tablet computers Software Mobile handsets Security Smart devices Wireless fidelity Identification of persons

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1563-1570页

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Analyze Decentralized Personal Health data using Solid, Digital Consent, and Federated Learning 14

Analyze Decentralized Personal Health Data using Solid, Digi...

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14th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2023

作者： Sun, Chang van Soest, Johan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands Brightlands Institute of Smart Society Faculty of Science and Engineering Maastricht University Maastricht Netherlands GROW School for Oncology and Reproduction Maastricht University Medical Centre Maastricht Netherlands

TIDAL is a Solid (SOcial Linked data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： data privacy

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