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IEEE International Conference on Systems, Man and Cybernetics

作者： Li Li Jingyang Hu Shihao Fu Xin Xing Wei Zhou School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science and Mathematics Anyang University Henan Province China Chongqing Academy of Metrology and Quality Inspection

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Existing rumor detection methods have not ade-quately considered which features should be addressed at the macroscopic or micro levels. Furthermore, the fundamental manifestation of news dissemination-public opinion-has received insufficient attention. We propose a macro-micro based public opinion (MMPO) modeling method to address these gaps. This approach enables a better understanding of news dissemination on social media platforms. Firstly, we incorporate post sentiment as a crucial feature. Secondly, we examine how the propagation at the micro-level influences sub-posts. Next, we investigate the temporal variations in public opinion from a macro perspective. Lastly, we integrate the acquired general propagation information with textual data to obtain more distinct representations of news propagation. By leveraging the macro perspective, the proposed model effectively captures the fine-grained characteristics of news propagation and helps smooth out the noise in user comments. The experimental evaluation showcases the superiority of our model in rumor detection compared to existing methods.

关键词： Social networking (online) Fuses Noise Nonhomogeneous media Market research Feature extraction Feedforward neural networks Cybernetics

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Adil Nawaz Faisal Saeed Victor C.M. Leung Jianqiang Li Zhaoxia Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Gastroenterology Shenzhen Children’s Hospital China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.

关键词： Pathology Accuracy Biological system modeling Noise Predictive models Reliability Decision trees Biological cells Cancer Biomedical imaging

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EA-YOLO: An Endangered Animal Detection Algorithm Incorporating Attention Mechanisms

EA-YOLO: An Endangered Animal Detection Algorithm Incorporat...

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International Conference on Computer engineering and Applications (ICCEA)

作者： Kai Yan Junping Qin Lei Wang Hao Sun Shulan Sun Xiaole Shi College of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service College of Data Science and Application Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Endangered animal resources are crucial for biodiversity conservation and research. The detection and identification of endangered animals is a significant means to achieve this. However, the recognition accuracy of endangered animals is often affected by complex environmental conditions in the field. This paper proposes an improved algorithm named Endangered Animals-You Only Look Once (EA-YOLO) to address the issues of low target recognition accuracy and lack of feature extraction caused by complex environmental conditions and small target proportions in the picture. The algorithm is based on YOLOv8. This paper proposes two improvements to YOLOv8. Firstly, a multi-scale object detection head is introduced to fuse more image feature information, enhancing the network's ability to perceive and localize small targets. Secondly, a Global Attention Mechanism (GAM) module is added to learn richer feature information. The network can focus on important features and ignore irrelevant ones, leading to improved performance. The EA-YOLO model was evaluated on the LoTE-Animal dataset through extensive experiments. Results indicate that the improved model achieved an average accuracy of 95%, which is a 9% improvement over the original algorithm while maintaining competitive detection accuracy.

关键词： Accuracy Attention mechanisms Head Animals Target recognition Fuses Computational modeling

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A Novel Adaptive Perturbation-Based Method for Long-Tailed Open Sets Recognition

SSRN

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SSRN 2023年

作者： Ngueilbaye, Alladoumbaye Zhang, Qin Yang, Hongye Zhang, Xiaolin Zhang, Peng Chen, Xiaojun Huang, Joshua Zhexue Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Real-world data often have a long-tailed and open-ended distribution. A reliable practical machine learning system need to learn from the majority classes and also generalize to minority *** achieve this, acknowledge the minority and the novel class samples, and avoid misclassifying them into one of the majority classes are of great significance. Long-tailed Open Set Recognition aims to learn from the naturally distributed long-tailed and open-ended data, by using the easy-gained head class samples to learn the fundamental information, and also try to improve the out-of-distribution generalization ability by identifying the tail class and open-set class samples as novelty to avoid the misclassification. The key challenges for long-tailed open set recognition is the tail and open-set sensitivity, since they can be easily biased towards dominant classes and perform ineffectively on close-set dataset classes. Inspired by techniques on open set recognition, in this paper, we propose a Class-aware Input Preprocessing (CLAIP) approach to improve the detection of the Out-of-distribution samples by perturbing the input images. In this approach, we employ two techniques to leverage its performance. First, we introduce the middle class samples as auxiliary data during the network training process to force each category's boundaries. Second, we add a Class-aware Input Preprocessing module that can be easily plugged into the testing process by perturbing the salient regions of the input images to further improve the out-of-distribution generalization. The experimental results show that CLAIP achieves high quality and significant performance compared to the existing solutions. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Cellular Network Optimization Using Unfolding-Based Graph Neural Networks

Cellular Network Optimization Using Unfolding-Based Graph Ne...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Pengcheng He Yijia Tang Fan Xu Qingjiang Shi School of Software Engineering Tongji University Shanghai China Peng Cheng Laboratory Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

The rapid development of wireless communication networks has driven the need for careful optimization of network parameters to achieve optimal performance. Traditional optimization approaches, however, face challenges in managing a vast number of tunable parameters and scalability across different scenarios. In this paper, we propose a novel bipartite graph neural network (Bi-GNN) approach for scalable cellular network optimization, focusing on network parameters including the azimuth angle and tilt angle of the antenna array, as well as the transmission power of the base station (BS). Our approach leverages the bipartite structure of cellular network topology and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, offering performance guarantees while maintaining a low computational overhead. Numerical results demonstrate that the Bi-GNN approach not only achieves a higher coverage ratio, but also significantly reduces the computational cost compared to existing methods. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. The proposed Bi-GNN approach has the potential to revolutionize cellular network optimization, paving the way for more efficient and scalable network solutions.

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Cross-Chain Privacy-Preserving for BIoMT With Designated Verifier Proxy Signature

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IEEE Internet of Things Journal 2025年第12期12卷 20042-20055页

作者： Li, Chaoyang Jiang, Bohao Dong, Mianxiong Chen, Yuling Huang, Min Xin, Xiangjun Ota, Kaoru Zhengzhou University of Light Industry College of Software Engineering Zhengzhou450001 China Muroran Institution of Technology Muroran050-8585 Japan Guizhou University State Key Laboratory of Public Big Data Guizhou Guiyang550025 China

Blockchain-enabled Internet of Medical Things (BIoMT) has received extensive attention and in-depth research to solve the centralized, data island problems with the rapid developments of blockchain-related technologies. However, many different chains with different data structures, consensus protocols, and cryptographic algorithms are constructed, which brings a new »data island» problem. Meanwhile, the cryptographic algorithms used in most current BIoMT systems are weak against quantum attacks. In this paper, a cross-chain privacy-preserving (CCPP) model and a designated verifier proxy signature (DVPS) scheme have been proposed. This CCPP model is equipped with the relay chain technology and DVPS to achieve secure cross-chain medical data-sharing among different BIoMT systems. The DVPS scheme is constructed with lattice theory, which can achieve signer proxy, designated user verification, and anti-quantum attack. Then, the security proof shows that the proposed DVPS can capture the security properties of correctness, unforgeability, the signer's anonymity, and non-transferability. The performance evaluations show that the cross-chain transactions are efficient and stable with the transaction number increasing, and the proposed DVPS is efficient about the key size, time consumption, and energy consumption. This work can also improve the privacy security of system users and medical data in BIoMT systems and promote the value play of medical data. © 2014 IEEE.

关键词： Lattice theory

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Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

Deep Learning based Multilabel Hateful Speech Text Comments ...

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2022 IEEE International Conference on Current Development in engineering and Technology, CCET 2022

作者： Defersha, Naol Bakala Abawajy, Jemal Kekeba, Kula College of Electrical and Mechanical Engineering Centre of Excellence for Hpc & Big Data Analytics Addis Ababa Science and Technology Software Engineering Department 16417 Ethiopia School of Information Technology Deakin University Faculty of Science Engineering and Built Environment GeelongVIC3220 Australia Centre of Excellence College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Department of Software Engineering Technology 16417 Ethiopia

ISBN: (纸本)9781665454155

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%. © 2022 IEEE.

关键词： Speech recognition

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SSLFusion: Scale & Space Aligned Latent Fusion Model for Multimodal 3D Object Detection 39

SSLFusion: Scale & Space Aligned Latent Fusion Model for Mul...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ding, Bonan Xie, Jin Nie, Jing Cao, Jiale School of Big Data and Software Engineering Chongqing University Chongqing China School of Microelectronics and Communication Engineering Chongqing University Chongqing China School of Electrical and Information Engineering Tianjin University Tianjin China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)157735897X

Multimodal 3D object detection based on deep neural networks has indeed made significant progress. However, it still faces challenges due to the misalignment of scale and spatial information between features extracted from 2D images and those derived from 3D point clouds. Existing methods usually aggregate multimodal features at a single stage. However, leveraging multi-stage cross-modal features is crucial for detecting objects of various scales. Therefore, these methods often struggle to integrate features across different scales and modalities effectively, thereby restricting the accuracy of detection. Additionally, the time-consuming Query-Key-Value-based (QKV-based) cross-attention operations often utilized in existing methods aid in reasoning the location and existence of objects by capturing non-local contexts. However, this approach tends to increase computational complexity. To address these challenges, we present SSLFusion, a novel Scale & Space Aligned Latent Fusion Model, consisting of a scale-aligned fusion strategy (SAF), a 3D-to-2D space alignment module (SAM), and a latent cross-modal fusion module (LFM). SAF mitigates scale misalignment between modalities by aggregating features from both images and point clouds across multiple levels. SAM is designed to reduce the inter-modal gap between features from images and point clouds by incorporating 3D coordinate information into 2D image features. Additionally, LFM captures cross-modal non-local contexts in the latent space without utilizing the QKV-based attention operations, thus mitigating computational complexity. Experiments on the KITTI and DENSE datasets demonstrate that our SSLFusion outperforms state-of-the-art methods. Our approach obtains an absolute gain of 2.15% in 3D AP, compared with the state-of-art method GraphAlign on the moderate level of the KITTI test set. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Graph-Augmented Contrastive Clustering for Time Series

SSRN

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SSRN 2023年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Zhang, Peng Chen, Junyang Chen, Xiaojun Huang, Joshua Zhexue Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

The recent emergence of time series contrastive clustering methods can be broadly categorized into two classes. The first class uses contrastive learning to learn universal representations for time series. Though they perform well in various downstream tasks, such methods disregard the important categorical information and clustering objective, leading to unsuitable representations of clustering tasks. The second class incorporates contrastive learning into the clustering objective. The potential connections and structures between the time series data are not fully explored during contrastive learning. To this end, we propose a graph-augmented contrastive learning framework called the "Time Series Graph-augmented Contrastive Clustering (TSGCC) method." We observed that the original samples should be similar to their augmentations and other samples in the same cluster. Hence, we used a weighted $KNN$ graph to build positive and negative sample pairs for contrastive learning. Subsequently, we projected the instance samples into a feature space with the dimensionality number of clusters and learned cluster-friendly features and cluster assignments by iteratively optimizing the contrastive loss. Experimental results demonstrate that TSGCC outperforms 16 advanced time-series clustering methods on 36 challenging UCR benchmarks, achieving the best clustering results on 12 datasets and the highest average rank (2.83) of RI overall methods. © 2023, The Authors. All rights reserved.

关键词： Time series

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Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging based on Adversarial Learning

Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging...

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IEEE International Conference on Image Processing

作者： Xiaoteng Chen Junying Chen School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education China

In plane wave medical ultrasound imaging, the analog echo signals are received via the transducer, and sampled as digital radio frequency data which is further processed by the beamforming algorithm to obtain the output ultrasound image. The sampling frequency of radio frequency data has a great impact on the image quality. The radio frequency data with higher sampling frequency is closer to the true distribution of the analog echo signals, leading to better image quality. However, high-frequency sampling chips are usually expensive. Thus, improving the plane wave imaging quality for radio frequency data with low sampling frequency has important practical significance. The ultrasound imaging quality is mainly affected by shallow features such as texture and color. However, low-sampling-frequency data has poor shallow features while high-sampling-frequency data has better shallow features. In this work, we propose an adversarial learning method to replace the shallow features of low-frequency data with the shallow features of high-frequency data, but retaining the deep features of low-frequency data. The experiment results demonstrate that the proposed method improves the plane wave imaging quality for low-sampling-frequency data by fusing shallow and deep features.

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