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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wei, Zhengyang Ye, Baoliu Cai, Miao Hohai University College of Computer and Software China Nanjing University Department of Computer Science and Technology China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798331509712

The learned index is a high-performance index structure that uses machine learning methods to predict key positions in a large key space efficiently. Existing learned indexes suffer from underfitting of key-to-position mapping, leading to poor lookup performance. This paper finds that a data distribution property in the widely-used composite key schema addresses this issue effectively. Specifically, the composite key consists of an agglomerate of attributes. Keys with the same attribute value have a regular data distribution, which leads to a higher fitness of key-to-position mapping. Applying the property, we introduce CK-index, a distribution-aware learned index for composite keys. CK-index divides the key space according to attribute values and trains each learned model separately for an attribute to achieve high fitness of key-to-position mapping. Furthermore, it achieves low data storage consumption via storing composite key's attributes instead of the whole keys. We evaluate the CK-index using real-world datasets. Evaluation results demonstrate that CK-index performs much better in lookup performance, bulk loading time and space consumption compared to B+Tree, RMI, PGM-index and ALEX. © 2024 IEEE.

关键词： Composite key Data distribution Learned index

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A software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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DeSAM2: Detection-Driven SAM2 for Hyperspectral Object Tracking 14

DeSAM2: Detection-Driven SAM2 for Hyperspectral Object Track...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： He, Wangquan Ren, Qi Zhang, Hongtao Mo, Ziqian Li, Nanying Xu, Meng Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798331513139

Hyperspectral object tracking (HOT) captures subtle object features, enabling precise identification and tracking in complex backgrounds. However, the high-dimensional nature of data and rapid object changes in dynamic scenes make accurate and robust tracking challenging. This paper proposes a detection-driven Segment Anything Model 2 (SAM2) for HOT (named DeSAM2), which enhances tracking accuracy and robustness through the collaboration of initial prompt tracking and a detection-based self-prompt auxiliary mechanism. Specifically, The method first employs a key band selection strategy to ensure that the spectral difference between object and background is maximized in complex backgrounds. Then, initial prompt tracking is used to acquire initial position of the object, and this process uses SAM2 as a base scheme to maintain high accuracy tracking. Furthermore, to address missed and false detections in initial tracking results within complex scenes, a detection-based self-prompt auxiliary mechanism is designed to further refine and correct tracking, thereby enhancing the adaptability and continuity of the model in dynamic environments. Experimental results on the datasets provided by the HOT Challenge 2024 indicate that the proposed method demonstrates superior performance compared to other advanced trackers. © 2024 IEEE.

关键词： Object detection

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Infrared Single Pixel Imaging Based on Generative Adversarial Network

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Journal of Shanghai Jiaotong University (science) 2023年 1-11页

作者： Jiang, Yilin Zhang, Yilong Zhang, Fangyuan College of Information and Communication Engineering Harbin Engineering University Harbin150001 China College of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

In the field of imaging, the image resolution is required to be higher. There is always a contradiction between the sensitivity and resolution of the seeker in the infrared guidance system. This work uses the rosette scanning mode for physical compression imaging in order to improve the resolution of the image as much as possible under the high-sensitivity infrared rosette point scanning mode and complete the missing information that is not scanned. It is effective to use optical lens instead of traditional optical reflection system, which can reduce the loss in optical path transmission. At the same time, deep learning neural network is used for control. An infrared single pixel imaging system that integrates sparse algorithm and recovery algorithm through the improved generative adversarial networks is trained. The experiment on the infrared aerial target dataset shows that when the input is sparse image after rose sampling, the system finally can realize the single pixel recovery imaging of the infrared image, which improves the resolution of the image while ensuring high sensitivity. © 2023, Shanghai Jiao Tong University.

关键词： Generative adversarial networks

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VR-Recon: Visual Refiner for Fine-Grained Human Reconstruction 4

VR-Recon: Visual Refiner for Fine-Grained Human Reconstructi...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Xie, Hong Zhu, Zibin Zhang, Xiaoyan Shen Zhen University College of Computer Science & Software Engineering Shenzhen China

ISBN: (纸本)9798350349252

To reconstruct 3D human figures with fine-grained details from sparse views, this paper proposes a novel framework called VR-Recon, which is based on a viewpoint refiner. In this framework, we first decouple geometric features from multi-scale feature learning by integrating an MSG image encoder, aiming to extract high-quality multi-scale geometric features. Next, we employ a viewpoint refiner to accurately capture critical fine-grained geometric information in each independent view space, thereby avoiding the weighted average fusion of features and eliminating the influence of invalid information. This method is trained using a consistent cosine loss and a cascaded view-space geometric loss. The former encourages the MSG to accurately perceive fine geometric changes from the image, while the latter helps the viewpoint refiner discern critical information in different view spaces. Experimental results demonstrate that, compared to state-of-the-art methods, VR-Recon exhibits robust performance in capturing fine geometric details. © 2024 IEEE.

关键词： Feature extraction

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Out-of-Distribution Detection with Prototypical Outlier Proxy 39

Out-of-Distribution Detection with Prototypical Outlier Prox...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Mingrong Chen, Chaoqi Sun, Qingqiang Wang, Yue Huang, Hui College of Computer Science and Software Engineering Shenzhen University China School of Engineering Great Bay University China Department of Computer Science University College London United Kingdom

ISBN: (纸本)157735897X

Out-of-distribution (OOD) detection is a crucial task for deploying deep learning models in the wild. One of the major challenges is that well-trained deep models tend to perform over-confidence on unseen test data. Recent research attempts to leverage real or synthetic outliers to mitigate the issue, which may significantly increase computational costs and be biased toward specific outlier characteristics. In this paper, we propose a simple yet effective framework, Prototypical Outlier Proxy (POP), which introduces virtual OOD prototypes to reshape the decision boundaries between ID and OOD data. Specifically, we transform the learnable classifier into a fixed one and augment it with a set of prototypical weight vectors. Then, we introduce a hierarchical similarity boundary loss to impose adaptive penalties depending on the degree of misclassification. Extensive experiments across various benchmarks demonstrate the effectiveness of POP. Notably, POP achieves average FPR95 reductions of 7.70%, 6.30%, and 5.42% over the second-best methods on CIFAR-10, CIFAR-100, and ImageNet-200, respectively. Moreover, compared to the recent method NPOS, which relies on outlier synthesis, POP trains 7.2 times faster and performs inference 19.5 times faster. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

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computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu Yang Yuan Pengcheng Ren Chengyun Song Fen Luo School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China College of Artificial Intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

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Zero-Shot Noise2Mean: Gap Minimization for Efficient Denoising from a Single Noisy Image 39

Zero-Shot Noise2Mean: Gap Minimization for Efficient Denoisi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Duo Shi, Yiqi Zhang, Guoyin Li, Sizhao Zhang, Liguo College of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)157735897X

Acquiring pairwise noisy-clean training data is challenging. Consequently, some self-supervised denoising methods utilize noisy image pairs as both input and target for network training. However, a major issue with these methods is the gap between the clean images of the input and target. In this paper, we achieve high-quality image denoising by reducing or even eliminating this gap. Our method requires no training data or prior knowledge of the noise distribution. It consists of two lightweight networks that can be trained using only a single noisy test image. Specifically, we propose a random mask-based downsampler that generates multiple pairs of downsampled noisy images, which are similar but distinct. These image pairs serve as the input for the first network, with the mean image of each pair used as the target. This initially reduces the gap between the clean images of the input and target. Particularly, in our method, the clean counterpart of the first network’s target (i.e., the mean image) can be obtained. We then train a second network using the mean image as input and its clean counterpart as the target. This effectively eliminates the gap and achieves better denoising results. Extensive experiments demonstrate that our method outperforms in both denoising performance and efficiency. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image denoising

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Improving 5G Positioning Through Signal-to-Noise Ratio Recognition Training

Improving 5G Positioning Through Signal-to-Noise Ratio Recog...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Wenqi Chen, Jianing Yang, Junze Chen, Chuhao Li, Wei Yadav, Rahul Meng, Xiangxu College of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Fifth-generation communication technology enables advanced indoor positioning with its high bandwidth and frequency capabilities. However, indoor environment variability causes signal propagation fluctuations, making existing models inadequate for accurate location estimation. In this paper, we demonstrate that integrating SNR recognition training with the positioning network can enhance localization performance. Specifically, by leveraging a general neural network to extract common features from 5G Channel State Information (CSI), we employ two carefully designed downstream networks for signal-to-noise ratio (SNR) estimation and location prediction. In order to improve localization performance, we utilize mutual information-based metric to direct each network head towards its specific task, thereby refining and enhancing the features pertinent to each task. Moreover, for enhanced adaptive positioning accuracy, the coefficients linked to the features used in SNR and position estimation are determined based on the hidden features of the corresponding identification head. These coefficients are then utilized to appropriately weigh the hidden features of the location regression module. Experimental results using datasets from real-world scenarios demonstrate that our proposed method significantly improves positioning accuracy over state-of-the-art (SOTA) methods. Code can be available at: https://***/mxx123321/indoor_snr. © 2025 IEEE.

关键词： 5G Channel State Information (CSI) Deep Learning (DL) Fingerprint Indoor positioning

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