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3rd International Conference on Information Science and System, ICISS 2020

作者： Anjum, Shaik Shabana Noor, Rafidah Md Ahmedy, Ismail Khamis, Norazlina Binti Anisi, Mohammad Hossein Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9781450377256

The technological advancements of the Internet of Things (IoT) in the recent past have facilitated immense progress towards mitigation of environmental pollution through smart transportation systems and solutions. In particular, communication to the commuters about the traffic ahead or occurrences of congestion has been envisioned to play a major role in outsmarting traffic through mobile applications giving rise to the emergence of the Internet of Mobile Things (IoMT). However, the existing mobile applications that serve as traffic reporting solutions still face major issues such as fixed route suggestions, longer delays during busy hours or emergencies, inefficient prompting of road accidents and heavy traffic en route to a particular destination. This research aims at providing solutions for notifying the commuters with updates on the traffic based upon the Air Quality Index (AQI) of the routes towards the destination and also about the approach of emergency vehicles. The cross-platform mobile application in this way enables the user to opt for a route with good air quality so that the more congested routes are avoided thereby mitigating the air pollution induced by road traffic. The experimental testing and validation of the proposed methodology are applied for areas belonging to Greater Kuala Lumpur. The various timings divided according to peak and non-peak hours are experimentally tested for analyzing the parameters of traffic usage and pattern through the mobile application. The outcome of the experiments has showed that when traffic flow is modelled and governed through vehicular emissions and concentrations of air pollutants, nearly 75% of the congested traffic is reduced thereby, giving rise to pollution-free environment as well as mitigation of urban heat island (UHI) effect that is formed through vehicular heat generation and difference in temperatures. On the other hand, the approach of emergency vehicles also prompts the commuters to avoid panic. © 20

关键词： Traffic congestion

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Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning

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IEEE Transactions on Network Science and engineering 2025年

作者： Chen, Xianfu Zhao, Zhifeng Mao, Shiwen Wu, Celimuge Zhang, Honggang Bennis, Mehdi Shenzhen CyberAray Network Technology Co. Ltd. Shenzhen518100 China Zhejiang University Zhejiang Lab Hangzhou310027 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States The University of Electro-Communications Meta-Networking Research Center Chofu-shi Tokyo182-8585 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Chofu-shi Tokyo182-8585 Japan City University of Macau Faculty of Data Science Taipa999078 China University of Oulu Centre for Wireless Communications Oulu90570 Finland

The age of information metric fails to correctly describe the intrinsic semantics of a status update. In an intelligent reflecting surface-aided cooperative relay communication system, we propose the age of semantics (AoS) for measuring the semantics freshness of status updates. Specifically, we focus on status updates from a source node (SN) to the destination, which is formulated as a Markov decision process. The objective of the SN is to maximize the expected satisfaction of AoS and energy consumption under the maximum transmit power constraint. To seek the optimal control policy, we first derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. However, implementing the online DAC in practice poses key challenge in infinitely repeated interactions between the SN and the system, which can be dangerous particularly during exploration. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system. Numerical experiments verify the theoretical results and show that our offline DAC scheme significantly outperforms the online DAC scheme and the most representative baselines in terms of mean utility, demonstrating strong robustness to dataset quality. © 2013 IEEE.

关键词： Deep reinforcement learning

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Attention-Guided Second-Order Pooling Convolutional Networks

Attention-Guided Second-Order Pooling Convolutional Networks

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shannan Chen Qiule Sun Cunhua Li Jianxin Zhang Qiang Zhang Key Lab of Advanced Design and Intelligent Computing Dalian University China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology China School of Computer Engineering Jiangsu Ocean University China School of Computer Science and Engineering Dalian Minzu University China

Recently, channel attention-guided convolutional networks (ConvNets) have shown great advance on visual recognition tasks. However, they mainly exploit coarse first-order statistics to characterize holistic image and rarely focus on long-range feature dependencies, which limits the representation power in a certain. To handle above limitations, this paper proposes a novel attention-guided second-order pooling convolutional network (ASP-Net). ASP-Net introduces bilinear pooling that captures pairwise feature interactions to model second-order statistics. Meanwhile, it explicitly collects long-range dependencies via non-local operations, thus providing a global view in lower layers. Then, the second-order statistics and non-local context features are fused to obtain the enhanced representation for predicting channel-wise attention map and scaling convolution features. Experiment results on three commonly used datasets illuminate that ASP-Net outperforms its counterparts and achieves competitive performance.

关键词： Convolutional codes Visualization Convolution Conferences Network architecture Acoustics Task analysis

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Fast Learning-Based Split Type Prediction Algorithm for VVC

Fast Learning-Based Split Type Prediction Algorithm for VVC

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IEEE International Conference on Image Processing

作者： Dayong Wang Liulin Chen Xin Lu Frederic Dufaux Weisheng Li Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications University of Electronic Science and Technology of China

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural networks for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average.

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model

arXiv

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arXiv 2024年

作者： Fu, Ming-Xiang Song, Yu Lv, Jia-Meng Cao, Liang Jia, Peng Li, Nan Li, Xiang-Ru Liu, Ji-Feng Luo, A-Li Qiu, Bo Shen, Shi-Yin Tu, Liang-Ping Wang, Li-Li Wei, Shou-Lin Yang, Hai-Feng Yi, Zhen-Ping Zou, Zhi-Qiang National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing101408 China Key lab of Space Astronomy and Technology National Astronomical Observatories Beijing100101 China School of Computer Science South China Normal University Guangzhou510631 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Beijing100101 China University of Chinese Academy of Sciences Nanjing211135 China University of Science and Technology Beijing Beijing100083 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Computer and Information Dezhou University Dezhou253023 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan30024 China School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China Nanjing University of Posts & Telecommunications Nanjing210023 China School of Science University of Science and Technology LiaoNing Anshan114051 China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. Astronomers are turning to deep learning techniques to address this, but the methods are limited by their specific training sets, leading to considerable duplicate workloads too. Hence, as an example to present how to overcome the issue, we built a framework for general analysis of galaxy images, based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more. Considering the low signal-to-noise ratio of galaxy images and the imbalanced distribution of galaxy categories, we have incorporated a Human-in-the-loop (HITL) module into our large vision model, which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively. The proposed framework exhibits notable few-shot learning capabilities and versatile adaptability to all the abovementioned tasks on galaxy images in the DESI legacy imaging surveys. Expressly, for object detection, trained by 1000 data points, our DST upon the LVM achieves an accuracy of 96.7%, while ResNet50 plus Mask R-CNN gives an accuracy of 93.1%;for morphology classification, to obtain AUC ∼ 0.9, LVM plus DST and HITL only requests 1/50 training sets compared to ResNet18. Expectedly, multimodal data can be integrated similarly, which opens up possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-message astronomy. © 2024, CC BY.

关键词： Object detection

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Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

arXiv

引用

arXiv 2023年

作者： Luo, Luyang Wang, Xi Lin, Yi Ma, Xiaoqi Tan, Andong Chan, Ronald Vardhanabhuti, Varut Chu, Winnie C.W. Cheng, Kwang-Ting Chen, Hao The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Radiation Oncology Stanford University School of Medicine United States The Department of Anatomical and Cellular Pathology The Chinese University of Hong Kong Hong Kong The Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong The Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Weeds Detection and Classification using Convolutional Long-Short-Term Memory

Research Square

引用

Research Square 2021年

作者： Arif, Sheeraz Kumar, Rajesh Abbasi, Shazia Mohammadani, Khalid H. Dev, Kapeel Faculty of Information Technology Barrett Hodgson University Sindh Karachi Pakistan School of Computer science and Technology Beijing Institute of Technology China Faculty of information and technology University of Sindh Sindh Jamshoro Pakistan School of Electronic Engineering Beijing University of Posts and Telecommunications 100876 China Department of Computer Science University of Sindh Sindh Jamshoro Pakistan

The smart agricultural robotic system can decrease the dependence on various traditional agriculture crop spraying methods such as pesticides, herbicides, and fertilizer. To meet the world population food requirements, conventional schemes are not sufficient for spraying agrochemicals to control the weeds and increase crop production. Therefore, a smart and intelligent farming system is introduced to increase the production of crops and to reach crop production target. In this paper, Deep Learning (DL) based algorithms is applied for the identification and classification of weed plants using combination of Convolutional Neural Networks (CNN) and Long-Short- Term Memory (LSTM). Convolutional Neural Networks (CNN) has a unique structure to get discriminative features for the input images, and LSTM allows to jointly optimize the classification. To validate the proposed scheme, nine kinds of weeds are classified using the proposed method such as vine weeds, three-leaf weeds, spiky weeds, and invasive creeping weeds. We carried out several extensive experiments and 99.36% of average classification accuracy is achieved. The obtained results show that the combination of CCN-LSTM has significantly higher classification capabilities in comparison to other existing prominent approaches. © 2021, CC BY.

关键词： Convolutional neural networks

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Jointly Subspace Hashing for Medical Image Retrieval 8

Jointly Subspace Hashing for Medical Image Retrieval

引用

8th International Conference on Digital Home, ICDH 2020

作者： Wang, Xiaoqin Chen, Chen Huang, Jiayu Lu, Linfa Li, Ji Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Chiang Mai University Department of Computer Science Faculty of Science Chiang Mai Thailand Guilin University of Electronic Technology Institute for Artificial Intelligence Interdisciplinary Research Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin China

ISBN: (纸本)9781728192345

The explosive growth of medical images brings about massive amounts of high-dimensional images. The medical image retrieval technique is effective in selecting some similar medical images for medical analysis. A vast majority of existing medical image retrieval methods aim to extract a single feature to represent a medical image. Although these methods have improved the performance, they ignore that different characteristics express different information and they are an indispensable part of content information. Moreover, conventional features consume much storage, which is not conducive to data storage. Therefore, to reduce the loss of information and save storage space, we propose a novel jointly subspace hashing (JSSH) method for medical image retrieval. We solve the high-dimensionality of medical by the simplest image segmentation technology, and then study different subspace projection matrices. Finally, we integrate these matrices into the hash learning model, and build an objective function and learn a series of discriminative binary codes. By conducting comprehensive experiments on three medical image benchmark datasets, we demonstrate the effectiveness of our proposed JSSH. © 2020 IEEE.

关键词： Image segmentation

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A Principled Design of Image Representation: Towards Forensic Tasks

arXiv

引用

arXiv 2022年

作者： Qi, Shuren Zhang, Yushu Wang, Chao Zhou, Jiantao Cao, Xiaochun The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Institute of Information Engineering Chinese Academy of Sciences Beijing China The Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China The State Key Laboratory of Internet of Things for Smart City China The Department of Computer and Information Science Faculty of Science and Technology University of Macau China The School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called dense invariant representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Image representation

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