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2nd Workshop on Complex Data Challenges in Earth Observation, CDCEO 2022

作者： Wang, Yifan Jiang, Shuguo Xu, Meng Zhang, Shuyu Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

Hyperspectral images (HSIs) have a wide field of view and rich spectral information, where each pixel represents a small area of the earth's surface. The pixel-level classification task of HSI has become one of the research hotspots in hyperspectral image processing and analysis. More and more deep learning methods have been proposed in recent years, among which convolutional neural network (CNN) is the most influential. However, it is difficult for CNN-based models to obtain the global receptive field in HSI classification task. Besides, most of the self-supervised training methods are based on sample reconstruction, and it is not easy to achieve effective use of unlabeled samples. In this paper, we propose a novel convolutional embedding module, combined with the Transformer blocks, which successfully improves the context-awareness while retaining the local feature extraction capability. Moreover, a new self-supervised task is designed to make more efficient use of unlabeled data. Our proposed pre-training task only masks the central token and reconstructs the central pixel from a learnable vector. It allows the model to capture the patterns between the central object and surrounding objects without labels. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Pixels

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Common Problems in software Requirement engineering Process: An Overview of Pakistani software Industry 4

Common Problems in Software Requirement Engineering Process:...

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4th International Conference on Advancements in Computational sciences, ICACS 2023

作者： Khalid, Sadia Rasheed, Uzair Muneer, Mishal Butt, Wasi Haider Mehmood, Rabia Qamar, Usman College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan University of Lahore Department of Computer Science and IT Lahore Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665463775

Requirement engineering is a major phase of software development process. A project's success mainly depends on an efficient and effective requirement engineering process. Practices have been defined to ensure successful requirement engineering of software projects. Yet the professionals face numerous issues during this phase. This paper explores the software requirement engineering practices from in the software industry of Pakistan. It highlights the common problems faced by the software professionals, as well as commonly deployed solutions and practices. © 2023 IEEE.

关键词： Requirements engineering

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Programming Education for Young People using the Falling-Puzzle Game, 'Puyo Puyo' 14

Programming Education for Young People using the Falling-Puz...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Saito, Daisuke Tian, Ruochen Washizaki, Hironori Fukazawa, Yoshiaki Waseda University Global Software Engineering Laboratory Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350399431

This study utilizes the game rules of a falling-puzzle game, developed as a consumer-oriented digital game, in programming education for young people. When digital games are used in programming education, they are often designed specifically for that purpose. In this study, we focused on the game rules of the consumer falling-object puzzle game, 'Puyo Puyo.' Learning impact was investigated on 23 Japanese elementary, junior high, and senior high school students. The results show that the concepts of branching and arraying in programming were taught effectively;thus, consumer digital game rules can be used as a case study for programming education. © 2023 IEEE.

关键词： E-learning

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A novel observation points‐based positive‐unlabeled learning algorithm

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1425-1443页

作者： Yulin He Xu Li Manjing Zhang Philippe Fournier‐Viger Joshua Zhexue Huang Salman Salloum Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) ShenzhenChina College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Computing National University of SingaporeSingaporeSingapore

In this study,an observation points‐based positive‐unlabeled learning algorithm(hence called OP‐PUL)is proposed to deal with positive‐unlabeled learning(PUL)tasks by judiciously assigning highly credible labels to unlabeled *** proposed OP‐PUL algorithm has three ***,an observation point classifier ensemble(OPCE)algorithm is constructed to divide unlabeled samples into two categories,which are temporary positive and permanent negative ***,a temporary OPC(TOPC)is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive ***,a permanent OPC(POPC)is finally trained based on the combination of original positive samples,permanent positive samples and permanent negative *** exhaustive experimental evaluation is conducted to validate the feasibility,rationality and effectiveness of the OP‐PUL algorithm,using 30 benchmark PU data *** show that(1)the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and(2)the permanent positive samples have a consistent probability distribution with the original positive ***,a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL *** demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.

关键词： artificial intelligence datamining,machine learning

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Driving Scene Understanding with Traffic Scene-Assisted Topology Graph Transformer 24

Driving Scene Understanding with Traffic Scene-Assisted Topo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Rong, Fu Peng, Wenjin Lan, Meng Zhang, Qian Zhang, Lefei National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Hong Kong University of Science and Technology Hong Kong Hong Kong Horizon Robotics Beijing China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798400706868

Driving scene topology reasoning aims to understand the objects present in the current road scene and model their topology relationships to provide guidance information for downstream tasks. Previous approaches fail to adequately facilitate interactions among traffic objects and neglect to incorporate scene information into topology reasoning, thus limiting the comprehensive exploration of potential correlations among objects and diminishing the practical significance of the reasoning results. Besides, the lack of constraints on lane direction may introduce erroneous guidance information and lead to a decrease in topology prediction accuracy. In this paper, we propose a novel topology reasoning framework, dubbed TSTGT, to address these issues. Specifically, we design a divide-and-conquer topology graph Transformer to respectively infer the lane-lane and lane-traffic topology relationships, which can effectively aggregate the local and global object information in the driving scene and facilitate the topology relationship learning. Additionally, a traffic scene-assisted reasoning module is devised and combined with the topology graph Transformer to enhance the practical significance of lane-traffic topology. In terms of lane detection, we develop a point-wise matching strategy to infer lane centerlines with correct directions, thereby improving the topology reasoning accuracy. Extensive experimental results on Openlane-V2 benchmark validate the superiority of our TSTGT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available at https://***/rongfu-dsb/TSTGT. © 2024 ACM.

关键词： Graph theory

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Progressive Semantic Enhancement Network for Hyperspectral and LiDAR Classification

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11327-11339页

作者： Xiyou Fu Xi Zhou Yawen Fu Pan Liu Sen Jia College of Computer Science and Software Engineering Shenzhen University Shenzhen China

The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data is gaining attention for its improved classification accuracy. However, effectively integrating the rich spectral information of HSI and the elevation features of LiDAR has remained a challenge in multimodal fusion. This article proposes a novel approach called progressive semantic enhancement network (PSENet) for hyperspectral and LiDAR classification based on a progressive joint spatial-spectral attention mechanism. PSENet mainly comprises two modules: the spatial grouping constraint (SAGC) module and the spectral weighting constraint (SEWC) module. The SAGC module extracts multiscale features in the spatial domain, while the SEWC module focuses on enhancing semantic features in spectral dimension. By gradually utilizing spatial and spectral constraint modules to progressively enhance feature extraction, PSENet integrates affluent information for a more refined classification of ground objects. Based on experimental results, it has been demonstrated that PSENet outperforms several most advanced methods on three datasets. The SAGC and SEWC modules proposed in PSENet enable the effective integration of the spatial, spectral, and elevation information from HSI and LiDAR, providing a promising way to perform classification more accurately. The source codes of this work will be publicly available at http://***/ .

关键词： Laser radar Feature extraction Hyperspectral imaging Data mining Semantics Attention mechanisms Accuracy Distance measurement Neural networks Data models

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Detection of Deepfakes: Protecting Images and Vedios Against Deepfake 21

Detection of Deepfakes: Protecting Images and Vedios Against...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tian, Ying Zhou, Wang Haq, Amin Ul Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

In recent years, with the rapid development of deep learning and computer vision technology, the forgery technology of images and videos has become increasingly mature, posing new challenges to information security and social stability. Behind the re-evolution of deepfake lies the rampant proliferation of fake content, which is used for election tampering, identity fraud, fraud, spreading fake news, and so on. To address these challenges, researchers are constantly exploring and developing image-based deepfake detection techniques, which aim to effectively identify and prevent deepfake content in images and videos. This article will introduce the current development status of deepfake detection technology, present its principles and methods in detail, and look ahead to its future development directions. © 2024 IEEE.

关键词： Convolutional neural networks

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Multiscale Spatial-spectral Joint Feature Learning for Multispectral and Hyperspectral Image Fusion 23

Multiscale Spatial-spectral Joint Feature Learning for Multi...

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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： Min, Zhichao Wang, Yifan Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781665494571

For RGB image super-resolution, usually operates on a single image. However, due to a large number of spectral bands and high dimensionality of the data in hyperspectral images (HSIs), it is difficult for a single image super-resolution to improve spatial resolution without spectral distortion. Therefore, it is necessary to fuse spatial features of multispectral images (MSIs) to improve spatial resolution. In this paper, we take advantage of the end-to-end advantages of deep learning and propose multiscale spatial-spectral joint feature learning (MSSJFL) for multispectral and hyperspectral image fusion. MSSJFL method utilizes different scale feature learning to exact single spatial feature, spectral feature, spatial-spectral joint feature, and then restore spectral information and spatial details via feature restoration module. Experimental results demonstrate that the proposed network architecture achieves better performance compared with state-of-the-art HSI and MSI fusion. The classification accuracy of the fusion result on the real HSI dataset is also closest to the ground truth. The code of MSSJFL is available at https://***/jx-mzc/MSSJFL. © 2021 IEEE.

关键词： Representation learning Deep learning Smart cities Superresolution Network architecture Feature extraction Image restoration

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VCApather: A Network as a Service Solution for Video Conference Applications 34

VCApather: A Network as a Service Solution for Video Confere...

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34th Workshop on Network and Operating System Support for Digital Audio and Video, NOSSDAV 2024

作者： He, Dongbiao Lin, Canshu Yu, Xian Westphal, Cedric Ming, Zhongxing Cui, Laizhong Zhou, Xu Garcia-Luna-Aceves, J.J. Sangfor Technologies Inc China College of Computer Science and Software Engineering Shenzhen University China Computer Network Information Center Chinese Academy of Sciences China China Electrical and Computer Engineering Department University of Toronto Canada Futurewei

ISBN: (纸本)9798400706134

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×. © 2024 ACM.

关键词： Network layers

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