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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Weipeng Jiang, Changkun Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781665468190

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task. © 2022 IEEE.

关键词： Forecasting

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Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks

arXiv

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arXiv 2024年

作者： Xu, Zhengjia Lyu, Dingyang Zhang, Jinghui College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China

As graphs grow larger, full-batch GNN training becomes hard for single GPU memory. Therefore, to enhance the scalability of GNN training, some studies have proposed sampling-based mini-batch training and distributed graph learning. However, these methods still have drawbacks, such as performance degradation and heavy communication. This paper introduces SliceGCN, a feature-sliced distributed large-scale graph learning method. SliceGCN slices the node features, with each computing device, i.e., GPU, handling partial features. After each GPU processes its share, partial representations are obtained and concatenated to form complete representations, enabling a single GPU's memory to handle the entire graph structure. This aims to avoid the accuracy loss typically associated with mini-batch training (due to incomplete graph structures) and to reduce inter-GPU communication during message passing (the forward propagation process of GNNs). To study and mitigate potential accuracy reductions due to slicing features, this paper proposes feature fusion and slice encoding. Experiments were conducted on six node classification datasets, yielding some interesting analytical results. These results indicate that while SliceGCN does not enhance efficiency on smaller datasets, it does improve efficiency on larger datasets. Additionally, we found that SliceGCN and its variants have better convergence, feature fusion and slice encoding can make training more stable, reduce accuracy fluctuations, and this study also discovered that the design of SliceGCN has a potentially parameter-efficient nature. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Multi-Agent Graph Reinforcement Learning based On-Demand Wireless Energy Transfer in Multi-UAV-aided IoT Network

arXiv

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arXiv 2023年

作者： Zhao, Ze Yu Che, Yueling Luo, Sheng Wu, Kaishun Leung, Victor C.M. College of Computer Science and Software Engineering Shenzhen University China

This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices’ own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs’ trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs’ self-attentions for their collaborative WET, we propose the multi-agent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices’ energy requirements. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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An Efficient Steganographic Approach for Securing Text Transmission Using S-Box in Cloud 2

An Efficient Steganographic Approach for Securing Text Trans...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Malik, Javeria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

In era of modern world and technology where everything is digitalized. Cloud computing emerged as one of the fastest growing parts of digital industry. With the Succor of cloud computing models, business accomplishes their objectives with least exertion when contrasted with traditional processing environment. In spite of these compensations, yet security of the information in cloud database is the significant fear in endorsement of cloud. Although several techniques have been proposed such as cryptographic and steganographic approaches, but all of these have some loopholes. Therefore, in this paper new data protection technique based on steganographic encryption is proposed which has less computational time and secure against known plain text and Man-In-Middle Attack. © 2023 IEEE.

关键词： Cryptography

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Carousel Storage and Picking Scheduling Issues: A Review

Carousel Storage and Picking Scheduling Issues: A Review

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qin, Shujin Yang, Xinyi Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal University Shangqiu476000 China Monmouth University Department of Computer Science and Software Engineering West Long BranchNJ07764 United States College of Information Science and Engineering Northeast University Shenyang110819 China College of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350337020

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions. © 2023 IEEE.

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Balancing Data Imbalance in Remote Sensing Image Road Segmentation with Gradient-Guided Multi-level Data Augmentation

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IEEE Signal Processing Letters 2025年 32卷 2404-2408页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian University of Science and Technology Liaoning College of Computer Science and Software Engineering Anshan China

In remote sensing image segmentation, data imbalance remains a critical challenge, particularly in datasets from networks and natural scenes, increasing task complexity. Recent studies have explored adversarial training to address this issue, but these methods rely heavily on labeled data, introducing annotation bias and efficiency concerns. To overcome these limitations, this paper proposes a gradient-guided image-level perturbation strategy. Specifically, we design a multi-level, multi angle data augmentation framework that integrates patch-level perturbation with gradient-based adaptive region selection, enhancing the model's recognition of rare samples while preserving contextual integrity. Experimental results demonstrate that our method surpasses traditional segmentation algorithms and data balancing strategies, achieving an F1 score of 82.67% on the Massachusetts Roads dataset and exhibiting strong generalization on the LoveDA dataset. This research provides valuable insights for advancing remote sensing image analysis. © 1994-2012 IEEE.

关键词： Data Imbalance Gradient Guidance Multi-level Data Augmentation Remote Sensing Image Segmentation Road Segmentation

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TBSR: Tile-Based 360° Video Streaming with Super-Resolution on Commodity Mobile Devices

TBSR: Tile-Based 360° Video Streaming with Super-Resolution...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lei Zhang Haobin Zhou Haiyang Wang Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science University of Minnesota Duluth USA

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Streaming 360° videos demands excessive bandwidth. Tile-based streaming and super-resolution are two widely studied approaches to alleviate bandwidth shortage and enhance user experience in such real-time video streaming systems. The former prioritizes the transmission of a fraction of the 360° video according to the user viewport, while the latter enhances the streamed video in higher resolutions through computations. However, these two approaches bring substantial complexity and computation overhead and thus suffer from resource bottlenecks due to the constrained mobile hardware. This paper proposes TBSR, a practical mobile 360° video streaming system that incorporates in-time super-resolution with tile-based streaming on commodity mobile devices. We present the designs of three key mechanisms, including a rate adaptation method with macro tile grouping to reduce decoding computations, a decoding and SR scheduler for different types of tasks to achieve the best cost efficiency, and the workload adjustment method to control the amount of tasks given the available capabilities. We further implement the TBSR prototype. Our performance evaluation shows that TBSR outperforms the existing methods, improving QoE quality by up to 32% and bandwidth savings by 26%.

关键词： Costs Superresolution Prototypes Bandwidth Streaming media Mobile handsets Decoding

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Deep3DSketch-im:rapid high-fidelity AI 3D model generation by single freehand sketches

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 149-159页

作者： Tianrun CHEN Runlong CAO Zejian LI Ying ZANG Lingyun SUN College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China School of Software Technology Zhejiang UniversityHangzhou 310027China School of Information Engineering Huzhou UniversityHuzhou 313000China

The rise of artificial intelligence generated content(AIGC)has been remarkable in the language and image fields,but artificial intelligence(AI)generated three-dimensional(3D)models are still under-explored due to their complex nature and lack of training *** conventional approach of creating 3D content through computer-aided design(CAD)is labor-intensive and requires expertise,making it challenging for novice *** address this issue,we propose a sketch-based 3D modeling approach,Deep3DSketch-im,which uses a single freehand sketch for *** is a challenging task due to the sparsity and ***3DSketch-im uses a novel data representation called the signed distance field(SDF)to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points,and a specially designed neural network that can capture point and local *** experiments are conducted to demonstrate the effectiveness of the approach,achieving state-of-the-art(SOTA)performance on both synthetic and real ***,users show more satisfaction with results generated by Deep3DSketch-im,as reported in a user *** believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.

关键词： Content creation Sketch Three-dimensional(3D)modeling 3D reconstruction Shape from X Artificial intelligence(AI)

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Usability Evaluation of Kids' Learning Apps 2

Usability Evaluation of Kids' Learning Apps

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ibrahim, Amer Al-Rajab, Murad Hamid, Khalid Aqeel, Muhamamd Muneer, Salman Parveen, Mehvish Saleem, Maryam American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Abu Dhabi University Computer Scienc and IT College of Engineering United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Superior University Department of Software Engineering Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NCBAE School of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids' fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically. © 2023 IEEE.

关键词： Learning systems

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