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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Jia Guo, Hanghui Shi, Weijie Chen, Zhangze De Meo, Pasquale The Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China Department of Computer Science University of Messina Italy

ISBN: (纸本)157735897X

The Dynamic Retrieval Augmented Generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). However, current dynamic RAG methods fall short in both aspects: identifying the optimal moment to activate the retrieval module and crafting the appropriate query once retrieval is triggered. To overcome these limitations, we introduce an approach, namely, RaDIO, Real-Time Hallucination Detection with Contextual Index Optimized query formulation for dynamic RAG. The approach is specifically designed to make decisions on when and what to retrieve based on the LLM’s real-time information needs during the text generation process. We evaluate RaDIO along with existing methods comprehensively over several knowledge-intensive generation datasets. Experimental results show that RaDIO achieves superior performance on all tasks, demonstrating the effectiveness of our work. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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REncoder: A Space-Time Efficient Range Filter with Local Encoder 39

REncoder: A Space-Time Efficient Range Filter with Local Enc...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Wang, Ziwei Zhong, Zheng Guo, Jiarui Wu, Yuhan Li, Haoyu Yang, Tong Tu, Yaofeng Zhang, Huanchen Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation Beijing China Tsinghua University Beijing China

ISBN: (纸本)9798350322279

A range filter is a data structure to answer range membership queries. Range queries are common in modern applications, and range filters have gained rising attention for improving the performance of range queries by ruling out empty range queries. However, state-of-the-art range filters, such as SuRF and Rosetta, suffer either high false positive rate or low throughput. In this paper, we propose a novel range filter, called REncoder. It organizes all prefixes of keys into a segment tree, and locally encodes the segment tree into a Bloom filter to accelerate queries. REncoder supports diverse workloads by adaptively choosing how many levels of the segment tree to store. We theoretically prove that the error of REncoder is bounded and derive the asymptotic space complexity under the bounded error. We conduct extensive experiments on both synthetic datasets and real datasets. The experimental results show that REncoder outperforms all state-of-the-art range filters. © 2023 IEEE.

关键词： Data structures

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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Personalized Confederation Training for Smart Iot Systems: A Cloud - Delivered Structure

Personalized Confederation Training for Smart Iot Systems: A...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Jitendra Kumar Katariya Nivesh Department of Computer Science Engineering Vivekananda Global University Department of Master of Computer Application Chandigarh School of Business Jhanjeri

The Internet of Things (IoT) has significantly impacted many facets of contemporary life, and several sophisticated IoT applications and services are already being developed. Recently, the concept of federated learning has been proposed in order to train a globally shared model on IoT devices utilising a large number of user-generated data samples while stopping data leak. Due to the widespread heterogeneities in device, statistics, and model in complex IoT contexts, traditional federated learning is not suitable for instant implementation. In this paper, we present a cloud-edge architecture for intelligent IoT applications that includes a customised federated learning framework. To address the heterogeneity worries in IoT conditions, we examine imaginative altered united learning calculations that could diminish the unfortunate results brought about by heterogeneities in various aspects. The advantages of edge registering empower smart IoT applications to meet the models for high handling pace and low inertness. At long last, we give a contextual investigation of human movement recognition in light of Web of Things to show the advantages of customisable combined learning for intelligent IoT applications.

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Research on Interactive Electronic Manual Based on VR Technology 21st

Research on Interactive Electronic Manual Based on VR Techno...

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21st Asia Simulation Conference, AsiaSim 2022

作者： Yongqi, Ma Xun, Cheng Qiyun, He Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang62100 China University of Electronic Science and Technology of China Sichuan Chengdu610000 China

ISBN: (纸本)9789811991943

Based on traditional IETM and VR technology, the concept of "VR + Manual" is proposed, and the main function design and four-layer architecture design of "VR + Manual" are given. The digital mapping data model is established between the IETM technical data and the digital prototype through the ontology data modeling method, and the seamless connection between the data model and the "digital prototype modeling and simulation engine" is realized. By using the integrating multi-class information visualization method, the visualization of multi-dimensional equipment technical status based on the digital prototype is realized. A data-driven interactive interface generation engine is proposed, which is oriented to five types of IETM data, such as description, procedure, fault, illustrated Parts, and maintenance plan. It realizes the rapid generation of interactive interfaces and the release of the application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Visualization

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jing Guo, Lijun Ni, Lixin He, Xiuchao Wang, Jianhua Zhang, Rong Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications. © 2023 IEEE.

关键词： Global Features Hip joint Hip Morphology-Aware Loss Landmark Detection Local Features

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A Dual-View Fusion Network for Automatic Spinal Keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Dandan Guo, Lijun Zhang, Rong He, Xiuchao Wang, Qiang Wang, Jianhua Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

关键词： biplane X-ray images dual-view feature fusion spinal keypoint detection

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Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

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computers in Biology and Medicine 2024年 181卷 109031-109031页

作者： Santosh Kumar Patra, P. Tripathy, Biswajit Research Scholar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela769015 India Professor Master of Computer Applications Einstein College of Computer Application and Management Odisha Khurda752060 India

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall. © 2024 Elsevier Ltd

关键词： COVID-19 pandemic Internet of things Remote patient monitoring Machine learning optimization Particle swarm optimization Black widow optimization Iterative deep convolution learning

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A stack autoencoders based deep neural network approach for cervical cell classification in pap-smear images

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Recent Advances in computer science and Communications 2021年第1期14卷 62-70页

作者： Singh, Sanjay K. Goyal, Anjali Department of Computer Science & Engineering I.K. Gujral Punjab Technical University Kapurthala India Department of Computer Application Guru Nanak Institute of Management and Technology Ludhiana India

Background: Early detection of cervical cancer may give life to women all over the world. Pap-smear test and Human papillomavirus test are techniques used for the detection and pre-vention of cervical cancer. Objective: In this paper, pap-smear images are analysed and cells are classified using stacked auto-encoder based deep neural network. Pap-smear cells are classified into 2 classes and 4 classes. Two-class classification includes classification of cells in normal and abnormal cells while four-class classification includes classification of cells in normal cells, mild dysplastic cells, moderate dysplastic cells and severe dysplastic cells. Methods: The features are extracted by deep neural networks based on their architecture. Proposed deep neural networks consist of three stacked auto encoders with hidden sizes 512, 256 and 128, respectively. Softmax used as the outer layer for the classification of pap smear cells. Results: Average accuracy achieved for 2-class classification among normal and abnormal cells is 98.2% while for 4-class classification among normal, mild, moderate and severe dysplastic cells is 93.8% respectively. Conclusion: The proposed approach avoids image segmentation and feature extraction applied by previous works. This study highlights deep learning as an important tool for cells classification of pap-smear images. The accuracy of the proposed method may vary with the different combination of hidden size and number of autoencoders. © 2021 Bentham science Publishers.

关键词： Cells

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Finding Simplex Items in Data Streams 39

Finding Simplex Items in Data Streams

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

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