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作者： Xu, Xiaolong Cao, Hao Geng, Qingfan Liu, Xihua Dai, Fei Wang, Chuanjian School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China College of Information Science and Technology Shihezi University Xinjiang China

Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software-defined network (SDN)-based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm-III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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Efficient Token Compression for Vision Transformer with Spatial Information Preserved

arXiv

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arXiv 2025年

作者： Mao, Junzhu Shen, Yang Guo, Jinyang Yao, Yazhou Hua, Xiansheng School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing100191 China Terminus Group Beijing100027 China

Token compression is essential for reducing the computational and memory requirements of transformer models, enabling their deployment in resource-constrained environments. In this work, we propose an efficient and hardware-compatible token compression method called Prune and Merge. Our approach integrates token pruning and merging operations within transformer models to achieve layer-wise token compression. By introducing trainable merge and reconstruct matrices and utilizing shortcut connections, we efficiently merge tokens while preserving important information and enabling the restoration of pruned tokens. Additionally, we introduce a novel gradient-weighted attention scoring mechanism that computes token importance scores during the training phase, eliminating the need for separate computations during inference and enhancing compression efficiency. We also leverage gradient information to capture the global impact of tokens and automatically identify optimal compression structures. Extensive experiments on the ImageNet-1k and ADE20K datasets validate the effectiveness of our approach, achieving significant speed-ups with minimal accuracy degradation compared to state-of-the-art methods. For instance, on DeiT-Small, we achieve a 1.64× speed-up with only a 0.2% drop in accuracy on ImageNet-1k. Moreover, by compressing segmenter models and comparing with existing methods, we demonstrate the superior performance of our approach in terms of efficiency and effectiveness. Code and models have been made available at https://***/ NUST-Machine-Intelligence-Laboratory/prune_and_merge. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media 16th

Impact of COVID-19 on Predicting 2020 US Presidential Electi...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Accurate and Complete Captions for Question-controlled Text-aware Image Captioning

Accurate and Complete Captions for Question-controlled Text-...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yehuan Wang Jian Hu Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

Question-controlled Text-aware Image Captioning (Qc-TextCap), is the task of generating a distinctive scene text aware caption according to several personalized questions when given an image. However, due to the diversity of visual scene, it is hard for current Optical Character Recognition (OCR) systems to extract complete scene text sentences from the images. Besides, existing works are limited in their use of question features and visual features. In this paper, we propose a Multimodal Transformer plus Scene text clustering and Cross modal attention (MTSC) to tackle the above challenges. We devise scene text clustering to group relevant scene text pieces which are detected as separate results by current OCR systems. To better utilize the information in questions and images, we design cross modal attention to enrich the features of both modalities. We extensively evaluate our model on the two Qc-TextCap datasets and superior results are achieved when comparing to state-of-the-art approaches.

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A Lightweight VK-Net Based on Motion Profiles for Hazardous Driving Scenario Identification 23

A Lightweight VK-Net Based on Motion Profiles for Hazardous ...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Zhen Xu, Jingning Zheng, Jiang Yu Fan, Hongfei Yu, Rongjie Zong, Jiaqi Li, Xinyi School of Software Engineering Tongji University Shanghai China Indiana University-Purdue University Department of Computer Science Indianapolis United States Jiangsu China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education Shanghai China College of Transportation Engineering Tongji University Shanghai China

ISBN: (纸本)9781665494571

Hazardous driving scenarios are critical for the development and testing of highly autonomous vehicles. Recent studies have proposed new approaches for extracting hazardous scenarios from driving videos, which greatly improve the detection accuracy compared with traditional approaches that extract information from structured kinematic data. However, direct processing on driving videos is costly, and the videos need to be uploaded and processed in a centralized manner. In this study, we propose a novel approach for reducing the cost of video analysis while maintaining high accuracy in hazardous scenario identification. Firstly, the videos are significantly compressed into motion profile images that contain motion trajectories of objects. Secondly, a lightweight CNN with attention mechanism has been designed to extract risky trajectory features of the conflicting traffic objects. Furthermore, data augmentation has been applied to increase the generalizability of the model. The proposed approach achieves significantly lightweight network size, and meanwhile, experimental results have demonstrated that the approach achieves high accuracy with sensitivity 0.95, specificity 0.85 and AUC 0.95, which outperforms state-of-the-art hazardous scenario identification approaches. The efficient execution of identification implies that it is suitable to be distributed and deployed at each vehicle, and consequently, videos could be processed via edge computing, which would improve the diversity of hazardous scenarios and the efficiency of scenario collection for further research work. © 2021 IEEE.

关键词： Sensitivity Image coding Smart cities Kinematics Feature extraction Trajectory Data mining

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques

Enhancing Medical Image-Based Diagnostics Through the Applic...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Haewon Byeon Rajit Nair V. Mahalakshmi Mohammed I. Khalaf Bharat Kaushik Mohammad Shabaz Department of AI and Software Inje University Gimhae Republic of Korea School of Computing Science & Engineering VIT Bhopal University Bhopal India Department of Computer Science College of Engineering and computer science Jazan University Jazan Saudi Arabia Department of computer science Al Maarif University College Al Anbar Iraq Masters University of Northern Iowa Lowa USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques.

关键词： Fluctuations Reviews Hospitals Measurement uncertainty Surgery Medical services Predictive models

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Deep Spatio-Temporal Decision Fusion Network for Facial Expression Recognition 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Xuanchi Yang, Heng Zhang, Xia Zheng, Xiangwei Li, Wei School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Internet Diagnosis and Treatment Center Taian City Central Hospital Taian China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (数字)9783031201028

ISBN: (纸本)9783031201011

Facial expression recognition (FER) becomes research focus in affective computing, as it already plays an important role in public security application scenarios such as urban safety management and safety driving assistance systems. Modeling the spatiotemporal information of facial expression sequences in a targeted manner, integrating and utilizing them appropriately is challenging. In this paper, a facial expression recognition method based on spatial-temporal decision fusion network (STDFN) is proposed. Firstly, the facial expression sequences are divided into four sub-sequences according to face regions, and BiLSTM are used for each of sub-sequences to extract local temporal features. The local morphological features of facial expressions can be captured in more detail to maximize the utilization of the temporal features of dynamic facial expressions. Then, VGG19 is utilized to extract the shallow spatial features of peak expression frame, and the channel weights of spatial features is assigned by squeeze-and-excitation module to attain the weighted spatial features. This allows valid spatial features to be purposefully retained to avoid overfitting. Finally, temporal features and spatial features are used separately calculating expression classification results. And a decision-level fusion module is designed to fuse the two results to obtain the final FER result. Extensive experimental results demonstrate that on three FER datasets CK+, Oulu-CASIA and MMI, achieves 98.83%, 89.31% and 82.86% accuracy, which proved that STDFN effectively improved the recognition accuracy of FER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Recognizing Human-object Interactions for a Home Assistive Robot 8

Recognizing Human-object Interactions for a Home Assistive R...

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8th International Conference on Automation, Control and Robotics engineering, CACRE 2023

作者： Wang, Liangliang Huo, Guanglei Chen, Xinwei Huang, Chengxi Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot - Xiaozhi II verify its robustness. © 2023 IEEE.

关键词： Human robot interaction

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ActivityMamba: A CNN-Mamba hybrid neural network for efficient human activity recognition

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IEEE Transactions on Mobile Computing 2025年

作者： Luo, Fei Li, Anna Jiang, Bin Khan, Salabat Wu, Kaishun Wang, Lu Great Bay University School of Computing and Information Technology Dongguan China Lancaster University School of Computing & Communications United Kingdom University of Petroleum College of Oceanography and Space Informatics Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Guangzhou China

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN's local and Mamba's global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition. © 2002-2012 IEEE.

关键词： Active learning

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Development of a Secure Cloud-based Breast Cancer Diagnosis System 22

Development of a Secure Cloud-based Breast Cancer Diagnosis ...

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6th International Conference on Cloud and Big Data Computing, ICCBDC 2022

作者： Amin, Fazal-E. Hussain, Muhammad Ali, Zulfiqar Busaleh, Mariam Al Sultan, Sarah A. Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh11541 Saudi Arabia School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Radiology College of Medicine King Khalid University Hospital King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9781450396578

Breast cancer is one of the most common cancers that cause death in women. Breast cancer was the most common cancer in 2020, with 2.26 million new cases. Cancer mortalities can be reduced by early detection and treatment. There are two components of early detection: early diagnosis and screening. Our work is related to early diagnosis. Early diagnosis helps to start treatment early, which is more effective and less expensive. The lives of cancer patients can be improved significantly by early detection and avoiding treatment delays. This paper presents the design and development of a secure cloud-based breast cancer diagnosis system. The proposed system uses using convolutional neural network for breast mass classification. Fragile zero watermarking will be implemented to secure the transmission of mammographs. The proposed system will enable women living in remote areas to get the facility for the routine initial screening near their homes without making their identity and medical data vulnerable. It will reduce the cost and time by avoiding frequent visits of the patients to specialized health care centers. On the other hand, it will help enhance the quality of service at specialized healthcare centers by reducing the number of patients in the initial screening. © 2022 ACM.

关键词： Deep learning

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