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arXiv 2023年

作者： Tang, Wei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

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Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification

arXiv

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arXiv 2024年

作者： Liu, Xin Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Lab. of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle NSW2308 Australia

Although attention-based multi-instance learning algorithms have achieved impressive performances on slide-level whole slide image (WSI) classification tasks, they are prone to mistakenly focus on irrelevant patterns such as staining conditions and tissue morphology, leading to incorrect patch-level predictions and unreliable interpretability. Moreover, these attention-based MIL algorithms tend to focus on salient instances and struggle to recognize hard-to-classify instances. In this paper, we first demonstrate that attention-based WSI classification methods do not adhere to the standard MIL assumptions. From the standard MIL assumptions, we propose a surprisingly simple yet effective instance-based MIL method for WSI classification (FocusMIL) based on max-pooling and forward amortized variational inference. We argue that synergizing the standard MIL assumption with variational inference encourages the model to focus on tumour morphology instead of spurious correlations. Our experimental evaluations show that FocusMIL significantly outperforms the baselines in patch-level classification tasks on the Camelyon16 and TCGA-NSCLC benchmarks. Visualization results show that our method also achieves better classification boundaries for identifying hard instances and mitigates the effect of spurious correlations between bags and labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In information Centric networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： information centric networking caching strategy two-layer caching model hierarchical division

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts

arXiv

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arXiv 2023年

作者： Jiang-Xin, Shi Tong, Wei Zhi, Zhou Jie-Jing, Shao Xin-Yan, Han Yu-Feng, Li National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Power control and task offloading strategies for high-density wireless body area networks based on deep reinforcement learning

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computer networks 2025年 267卷

作者： Liao, Yang Zhou, Huayang Leng, Chengfeng Su, Zhenlang Qin, Tuanfa School of Computer and Electronic Information Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning530004 China

With the widespread application of Wireless Body Area network (WBAN) in healthcare, issues such as a lack of computational resources and communication interference in high-density WBAN scenarios have become increasingly prominent. To address these issues, this paper introduces Mobile Edge Computing (MEC) to tackle the lack of computational resources and employs power control to mitigate communication interference among WBANs. We describe the joint optimization problem of power control and task offloading in high-density WBAN scenarios as a Markov Decision Process (MDP) and propose a Power Control and Task Offloading (PCTO) algorithm based on the Deep Deterministic Policy Gradient (DDPG) method to achieve coordinated optimization of transmission power and task offloading. Furthermore, we improve the algorithm's learning efficiency and sample utilization by incorporating a prioritized experience replay mechanism. By constructing a multi-objective reward function that comprehensively considers delay, energy consumption, and Signal-to-Interference-plus-Noise Ratio (SINR), and incorporating Quality of Service (QoS) constraints, the algorithm is capable of making intelligent decisions in complex wireless communication environments. The simulation results demonstrate that, compared to existing methods, the proposed approach significantly reduces system delay while improving SINR and service satisfaction in various WBAN density scenarios. © 2025 Elsevier B.V.

关键词： Deep reinforcement learning

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Research on the Application of Digital Signature in University Electronic Government System 6

Research on the Application of Digital Signature in Universi...

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6th International Conference on computer network, Electronic and Automation, ICCNEA 2023

作者： Wang, Tianma Zhao, Dongmei Qi, Jianjun North China Electric Power University International Education Institute Beijing China Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

ISBN: (纸本)9798350305388

This article analyzes the principles and processes of digital signature technology, as well as its application methods in the university e-government system. On the one hand, it meets the business management needs of university, and on the other hand, the application of digital signature and timestamp electronic signature ensures the security of university e-government SystemThe university e-government management system based on digital signature technology verifies the identity of users such as the sender and receiver of files, and issues digital certificates. The sender uses digital certificates for system authorization. Signs and encrypts confidential files such as documents through digital signatures to protecting the security of electronic file transmission. Upload the digitally signed and encrypted documents to the system, which completes the sending and receiving of the documents. On the one hand, the recipient downloads documents through the system and uses digital signatures to verify the integrity and authenticity of the documents. On the other hand, digitally sign the document and upload it to the system. Both parties complete the distribution and upload of documents through the system. © 2023 IEEE.

关键词： Authentication

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Classification of Zhuang Dialect combined with Bert and SimAM

Classification of Zhuang Dialect combined with Bert and SimA...

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

作者： Min Huang Xuejun Zhang Wenkang Chen School of Computer Electronics and Information Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Electrical Engineering Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Language recognition refers to the automatic recognition of the language type of the given speech segments. Zhuang is an important language in Southeast Asia, with more than 16 million native speakers. Due to the scarcity of data resources, there is relatively little research about Zhuang in the field of natural language processing, especially in the area of speech-level exploration. Because of the existence of a variety of dialects in Zhuang, this paper is the first to create a speech dataset for the study of the Zhuang language. In the experiment, it was observed that the output of different encoder layers of the BERT has an impact on classification, so this paper innovatively proposes to integrate the SimAM attention mechanism with the BERT model. The model uses a phone recognizer to extract phone sequences from speech segments. Then the Bert model combined with the integrated SimAM attention mechanism performs deep feature representation learning on the semantic information and word order of the sequence. Finally, the classifier realizes the efficient classification of the Zhuang language. Compared with Baseline Systems, the accuracy of the dialect recognition model proposed in this paper is 91.37%, which fully proves the effectiveness of the model.

关键词： Representation learning Attention mechanisms Semantics Speech recognition Bidirectional control Signal processing Feature extraction Encoding Natural language processing Speech processing

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NPPlanner: A Tool for National Park Planning at Scale Form Plant Diversity 6

NPPlanner: A Tool for National Park Planning at Scale Form P...

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6th IEEE information Technology, networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Hu, Zhilong Meng, Zhen Yi, Xiujuan Wang, Xuezhi Zhao, Lina Ye, Jianfei Chen, Zhiduan Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences State Key Laboratory of Systematic and Evolutionary Botany Institute of Botany Beijing China

ISBN: (纸本)9781665460033

Biodiversity is vital to human survival and has become a global issue. The establishment of national parks is regarded as the most straight and effective means for biodiversity conservation. Although the theory of national park planning has been well studied, there is still a lack of an available national park planning tool. In this paper, NPPlanner, a tool for national park planning at scale is proposed and implemented. It integrates the information of plants and the regional data of China and implements the visualization of plant distribution and the national park planning as a set of microservices by employing WebGIS and other techniques. Meanwhile, the method combining the green index and protective index is presented to better represent the regional biodiversity. The query for plant distribution and planning of national park will be visualized and the results are available to export for further work. NPPlanner is freely accessible at http://***/NPPlanner. © 2023 IEEE.

关键词： Visualization

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Deep anomaly detection on attributed networks by graph update

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Computing 2025年第6期107卷 1-25页

作者： Li, Yifan Li, Jiayin Liu, Ximeng Xu, Li College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou China College of Mathematics and Computer Science Fuzhou University Fujian Fuzhou China University Key Laboratory of Information Security of Network Systems Fuzhou University Fujian Fuzhou China

Anomaly detection on attributed networks has become extremely important as attributed networks are modeled in various real-world scenarios such as financial, communication, and social networks. Traditional anomaly detection methods such as degree-based or ego-network-based methods do not address well the complex interactions of attributes and structures in graph structures. Nowadays, graph autoencoder-based methods are a dominant graph anomaly detection method as the deep learning approach that better captures the complex interactions of graph data. However, the current approach ignores how to change the structure of the graph to improve the performance of graph anomaly detection leading to less than optimal anomaly detection results because there are many invalid or even obstructive edges in the original graph, which largely affect the results of graph anomaly detection. To address the above problems, we propose an applicable graph update method for graph anomaly detection, and we combine this method with that of a graph autoencoder to construct a new unsupervised graph anomaly detection framework to better utilize the updated graph structure. Specifically, we first make the anomalous nodes easier to distinguish by making similar nodes connected and removing unreasonable edges through graph updating. Then we use a graph autoencoder to reconstruct the error on the updated graph. Meanwhile the nodes with large errors we consider to be anomalous nodes. Particularly, when reconstructing the error, we reconstruct the original node attributes through the attributes of similar neighbors, which can better utilize the updated structure to improve the performance of anomaly detection. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all five real-world benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.

关键词： Unsupervised learning

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Don’t Abandon the Primary key: A High-Synchronization and Robust Virtual Primary key Scheme for Watermarking Relational Databases 26th

Don’t Abandon the Primary Key: A High-Synchronization and...

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26th International Conference on information and Communications Security, ICICS 2024

作者： Yang, Ke Yuan, Shuguang Yu, Jing Wang, Yuyang Yang, Tengfei Chen, Chi Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (纸本)9789819788002

A relational database is an infrastructure that manages and shares structured data. To safeguard the copyrights of data within such databases, database watermarking is an effective technique. Most watermarking schemes rely on the primary key (PK) to locate and embed watermarks, which preserves high watermark synchronization during detection. However, these schemes become invalid once PK is erased or changed. To avoid this vulnerability, virtual primary key (VPK) schemes are proposed to replace PK. Nevertheless, duplicate values in virtual primary keys compromise synchronization. Besides, current VPK schemes fail to utilize the primary key, even when it remains unchanged. This strategy decreases detection accuracy in many cases. In addition, we find an attribute name attack. It is a common challenge of existing watermarking schemes, which distort the link between detected attributes and watermarked attributes. In this paper, we propose a high-synchronization and robust VPK scheme. It introduces a classifier to maintain the original order and number of attributes to resist attribute name attacks. To resist primary key erasure or change and mitigate synchronization problems, it generates distinct virtual primary keys. Moreover, to improve detection accuracy, our watermarking scheme integrates the primary key when it remains unchanged. Experiments demonstrate that our scheme achieves high watermark synchronization. It is robust against various attacks, even when 98% of tuples or over half of the attributes are distorted. It is also practical in terms of data distortion and overhead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Relational databases Virtual primary key Watermarking

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