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arXiv 2023年

作者： Zhang, Taolin He, Sunan Dai, Tao Chen, Bin Wang, Zhi Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China Harbin Institute of Technology Shenzhen China Research Center of Artificial Intelligence Peng Cheng Laboratory China

In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks. However, when extended to point cloud data, existing works mainly focus on building task-specific models, and fail to extract universal 3D vision-language embedding that generalize well. We carefully investigate three common tasks in semantic 3D scene understanding, and derive key insights into the development of a pre-training model. Motivated by these observations, we propose a vision-language pre-training framework 3DVLP (3D vision-language pre-training with object contrastive learning), which transfers flexibly on 3D vision-language downstream tasks. 3DVLP takes visual grounding as the proxy task and introduces Object-level IoU-guided Detection (OID) loss to obtain high-quality proposals in the scene. Moreover, we design Object-level Cross-Contrastive alignment (OCC) task and Object-level Self-Contrastive learning (OSC) task to align the objects with descriptions and distinguish different objects in the scene, respectively. Extensive experiments verify the excellent performance of 3DVLP on three 3D vision-language tasks, reflecting its superiority in semantic 3D scene understanding. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Explainable Pulmonary Disease Diagnosis with Prompt-Based Knowledge Extraction

Explainable Pulmonary Disease Diagnosis with Prompt-Based Kn...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haodi Zhang Chenyu Xu Jiahong Li Peirou Liang Xiangyu Zeng Hao Ren Weibin Cheng Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Second Provincial General Hospital Institute for Healthcare Artificial Intelligence Application Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recent studies show that deep learning models perform well in many medical tasks such as medical imaging and automated diagnosis. With qualified training datasets, some models can achieve or even surpass expert-level performance on some tasks. However, as a typical black-box-style approach, deep learning lacks theoretical interpretability, which is especially important for medical tasks. On the other hand, there are many sources of domain knowledge for medical diagnosis from human experts, such as clinical guidelines. How to sufficiently integrate human knowledge in the model is crucial for explainable diagnosis. In this paper, we propose a novel framework for explainable automated diagnosis that leverages explicit medical knowledge. We automate the knowledge extraction from textual clinical guidelines with prompt-based learning, train a set of weighted first-order logical rules with constructed evidence database, and finally infer the diagnosis result with integrated knowledge and multi-sourced data. We instantiate the framework for pulmonary disease diagnosis, and our experiments on a real dataset show that our method outperforms the state-of-the-art baselines in accuracy and interpretability.

关键词： Deep learning Training Databases Pulmonary diseases Medical diagnosis Data mining Task analysis

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Better Queries for Aspect-Category Sentiment Classification 19

Better Queries for Aspect-Category Sentiment Classification

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19th China National Conference on Computational Linguistics, CCL 2020

作者： Li, Yuncong Yin, Cunxiang Zhong, Sheng-hua Zhong, Huiqiang Luo, Jinchang Xu, Siqi Wu, Xiaohui Baidu Inc. Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783030630300

Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM. © 2020, Springer Nature Switzerland AG.

关键词： Embeddings

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WFTNET: EXPLOITING GLOBAL AND LOCAL PERIODICITY IN LONG-TERM TIME SERIES FORECASTING

arXiv

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arXiv 2023年

作者： Liu, Peiyuan Wu, Beiliang Li, Naiqi Dai, Tao Lei, Fengmao Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China Ping An Bank Co. Ltd China WeBank Institute of Financial Technology Shenzhen University China

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline. Code is available at https://***/Hank0626/WFTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Wavelet transforms

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Active Neighbor Exploitation for Fast Data Aggregation in IoT Sensor Networks

TechRxiv

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TechRxiv 2023年

作者： Vo, Van-Vi Le, Duc-Tai Raza, Syed M. Kim, Moonseong Choo, Hyunseung The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of The Department of IT Convergence Software Seoul Theological University Bucheon14754 Korea Republic of

Fast data aggregation is crucial to facilitate critical Internet of Things (IoT) services as it collects all sensory data under restricted volume and time using in-network computation. The minimum latency data aggregation scheduling problem has attracted a lot of attention for the last decade. Existing approaches for the problem schedule all data transmissions based on an aggregation tree which was typically constructed without secondary interference awareness. Such interference causes numerous delay when scheduling a transmission from a node to its parent in the tree. When a sensor is active at multiple timeslots in a working period, the secondary interference can simply be omitted by scheduling a node to transmit its data to an active neighbor instead of its parent. This paper presents an Active Neighbor EXploitation (ANEX) approach which enables sensor nodes to switch their parents by discovering active neighbors for possible connectivity, regardless of the receivers established in the tree. Furthermore, the scheme prioritizes scheduling nodes with the fewest unscheduled active neighbors in order to execute more concurrent transmissions. We evaluate ANEX by theoretical analysis and extensive simulations with various scenarios. The results show that ANEX outstandingly achieves faster aggregation than the state-of-the-art approach by up to 86% while having an equivalent time complexity. © 2023, CC BY.

关键词： Internet of things

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Fast Finite-Time Adaptive Neural control for Pure-Feedback Stochastic Nonlinear Systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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Memristive Computing and Applications (ICMCA), IEEE International Conference on

作者： Ximei Zhao Hanyu Zhang Lijun Shao Lili Huang Jiao Li School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Collaborative Innovation Center of Memristive Computing Application(CICCA) Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality.

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Evaluating Fault Tolerance and Scalability in Distributed File Systems: A Case Study of GFS, HDFS, and MinIO

arXiv

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arXiv 2025年

作者： Malhotra, Shubham Yashu, Fnu Saqib, Muhammad Mehta, Dipkumar Jangid, Jagdish Dixit, Sachin Rochester Institute of Technology Dept. of Software Engineering United States Stony Brook University Dept. of Computer Science United States Texas Tech University Dept. of Computer Science United States C.K.Pithawalla College of Eng. and Tech India Infinera Corp United States Stripe Inc United States

Distributed File Systems (DFS) are essential for managing vast datasets across multiple servers, offering benefits in scalability, fault tolerance, and data accessibility. This paper presents a comprehensive evaluation of three prominent DFSs—Google File System (GFS), Hadoop Distributed File System (HDFS), and MinIO—focusing on their fault tolerance mechanisms and scalability under varying data loads and client demands. Through detailed analysis, how these systems handle data redundancy, server failures, and client access protocols, ensuring reliability in dynamic, large-scale environments is assessed. In addition, the impact of system design on performance, particularly in distributed cloud and computing architectures is assessed. By comparing the strengths and limitations of each DFS, the paper provides practical insights for selecting the most appropriate system for different enterprise needs, from high-availability storage to big data analytics. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

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Optimal Sliding Mode control of Single Arm PAM-Actuated Manipulator

Optimal Sliding Mode Control of Single Arm PAM-Actuated Mani...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Sadiq, Musaab Esam Humaidi, Amjad Jaleel Kadhim, Saleem Khalefa Mhdawi, Ammar Al Alkhayyat, Ahmed Ibraheem, Ibraheem Kasim General Company for Grain Processing Ministry of Trade Baghdad Iraq University of Technology Control and Systems Eng. Dept. Baghdad Iraq Edge Hill University Department of Computer Science United Kingdom College of Technical Engineering Islamic University Najaf Iraq College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781665437660

The manipulators actuated by Pneumatic Artificial Muscles (PAMs) are characterized by high nonlinearity and time varying of their coefficients. Therefore, nonlinear and robust controllers are required to cope with these challenging control problems. This work presents the development of control design for trajectory tracking of PAM-Actuated manipulator based on Sliding Mode control (SMC). The PSO technique is used to tune the design parameters of proposed controller for further enhancement of controlled PAM system performance. A comparison study has been conducted between the optimal and non-optimal SMC controllers via computer simulation using MATLAB programming software. © 2021 IEEE.

关键词： Trajectory tracking System performance Programming Muscles Manipulators Systems engineering and theory Numerical simulation

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Dense Network-Based Spectral-Polarization Image Fusion: Multispectral Data Enhancement via Encoder-Decoder Approach 6

Dense Network-Based Spectral-Polarization Image Fusion: Mult...

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6th International Conference on Information Technologies and Electrical engineering, ICITEE 2023

作者： Karim, Shahid Tong, Geng Li, Jinyang Yu, Yiting Ibrar, Muhammad Mehmood, Faisal Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China College of Mechanical Engineering Northwestern Polytechnical University Xi'an710072 China Key Laboratory of Micro/Nano Systems for Aerospace Ministry of Education Northwestern Polytechnical University Xi'an710072 China Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an701172 China Research Center of Smart Sensing Chips Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Software College Shenyang Normal University Shenyang China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen518055 China

ISBN: (纸本)9798400708299

Images are obtained by perceiving the sunlight reflected by objects or scenes, and due to limited solar irradiance, spatial resolution certainly decreases. In contrast, multispectral sensors hold the spatial information of ground objects even though the obtained images only have a few bands, which means that the obtained images have high spatial but insufficient spectral resolution. To overcome these limitations, we have developed a spectral and polarization image fusion technique that improves several perspectives of image fusion, such as intensity, edges, textures, and substantial information. Our proposed method utilizes an encoder-decoder approach, which contains dense and convolutional blocks to simulate the source images. The polarization images have been used to develop DoLP images that help to improve fused images' quality. We can perceive the visual quality of fused images from the experimental results. © 2023 ACM.

关键词： Image fusion

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An Improved Sudoku Solver

An Improved Sudoku Solver

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Adiba Afif Suha Binta Wadud M. Abdullah-Al-Wadud Bangladesh International School English Section Riyadh Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Sudoku is a popular puzzle game played by people of all ages. Consequently, many methods are developed to solve these puzzles on computer. However, these methods have deficiencies, especially in terms of the time taken to solve the puzzles. In this paper, we propose a method which is more efficient than the methods used before. This uses a few additional data structures and updated algorithms to make the method faster at solving various Sudoku puzzles.

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