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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 lab.ls. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

arXiv

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arXiv 2025年

作者： Wang, Zhenglin Wu, Jialong Li, Pengfei Jiang, Yong Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Tongyi Lab Alibaba Group China

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Joint Optimization of UAV Deployment and Task Computation Offloading Decision in UAV-assisted Edge Computing network

Joint Optimization of UAV Deployment and Task Computation Of...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yichuan Liu Jinbin Tu Yun Wang Key Lab of Computer Network and Information Integration MOE School of Computer Science and Engineering Southeast University Nanjing China

The UAVs' deployment decision and task computation offloading decision in the UAV-assisted edge computing network significantly impact the operating efficiency of edge network. On the basis of this, the Optimization Model for UAV Cluster Deployment and Computation Offloading Decision (OMUCDCOD) is established. The model jointly optimizes the number, location of UAV s, and task computation offloading decision. Different from previous studies, this model regards the terminal devices in the edge network as virtual MEC servers, and introduces the collab.rative computing mode. The task computation offloading decision made can effectively utilize the computing capabilities offered by the edge network. Considering that the two problems of UAV deployment decision and task computation offloading decision are intricately interconnected, we propose a two-layer optimization algorithm combining K-Means and ant colony algorithm (ToKmAc) to solve OMUCDCOD. ToKmAc is divided into upper and lower layers to solve this optimization problem. The upper layer uses K-Means to solve the UAV deployment decision, that is, the number and location of UAVS; the lower layer employs ant colony algorithm to solve computation offloading decision. Finally, extensive experiments verify the effectiveness of OMUCDCOD and ToKmAc.

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T-Sorokin: A General Mobility Model in Opportunistic networks

T-Sorokin: A General Mobility Model in Opportunistic Network...

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International Conference on computer Supported Cooperative Work in Design

作者： Jinbin Tu Qing Li Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration MOE Nanjing China

The opportunistic networks are a kind of ad hoc networks that rely on the chance of nodes meeting to transmit messages. Acting as an effective supplement to 4G and 5G networks in some special scenarios where hardware devices are limited, the opportunistic networks have a significant application in health monitoring, warning broadcasting, disaster relief, and so on. The mobility model is one of the research focuses on the opportunistic networks. On the basis of the social mobility theory proposed by Sorokin, a general mobility model, which is suited for various scenarios, called T-Sorokin is proposed. This model is described as a seven-tuple and implemented on the Opportunistic network Environment simulator and fits both Infocom06 and Rome taxi data set, which includes different areas ranging from hotel to city and different mobile units ranging from person to taxi. The results of experiments demonstrate that the T-Sorokin model has the advantage of generality, simplicity, and accuracy. It can simply establish movement tracks close to real data under different scenarios.

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A Non-Intrusive and Real-Time Data Provenance Method for DDS Systems

A Non-Intrusive and Real-Time Data Provenance Method for DDS...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Siyi Wei Jinbin Tu Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration MOE Nanjing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In the era of big data, because of the escalating data security threats, the importance of data provenance is widely recognized. It facilitates the tracking of data origins and evolution, improving the ability to diagnose errors and detect potential malicious attacks within large-scale software systems. However, conventional log-based provenance methods usually collect data intrusively, leading to strong coupling with the system. Meanwhile, although there are some log-based provenance systems that support runtime analysis, they exhibit inadequate real-time analysis performance. This paper proposes a non-intrusive and real-time approach to data provenance in DDS systems. The data capture is achieved with the help of the Data-Centric Publisher-Subscriber model of DDS, thus avoiding source code modification to the existing system. The collected data is then described in PROV-DDS model to generate RDF data. RDF datasets inherently form a directed and lab.led graph. The query over RDF datasets is decomposed into start subqueries to speed up processing with star subgraph signature. The experimental results show that our method provenances effectively with less than 1% time overhead and a lower query response time than RDF-3X and Redland.

关键词： Runtime Source coding Data security Stars Software systems Resource description framework Real-time systems

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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ACDNet: Attention-guided Collab.rative Decision network for Effective Medication Recommendation

arXiv

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arXiv 2023年

作者： Mi, Jiacong Zu, Yi Wang, Zhuoyuan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China

Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient’s medication records and specific medicines. Therefore, an Attention-guided Collab.rative Decision network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collab.rative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios. © 2023, CC BY.

关键词： Data mining

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KGRN: Knowledge Graph Relational Path network for Target Prediction of TCM Prescriptions 17th

KGRN: Knowledge Graph Relational Path Network for Target Pre...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Gong, Zhuo Zhang, Naixin He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Nanjing210018 China

ISBN: (纸本)9783030845315

Accurately predicting the effect targets of Traditional Chinese Medicine (TCM) prescriptions is essential for exploring the molecular mechanism and clarifying the mechanism of TCM. Although some scholars have used network pharmacology to study the target prediction of TCM prescriptions, all of them are for the analysis of a specific prescription-disease association, and the universal method for predicting effect targets of TCM prescriptions has not discovered yet. Therefore, this paper proposes a Knowledge Graph Relational Path network for Target Prediction of TCM Prescriptions (KGRN), which can integrate prescription’s molecular and target information through the Knowledge Graph (KG). At the same time, the semantic information and high-level structure of the knowledge graph are extracted through Knowledge Graph Embedding and Graph Neural networks, which can effectively capture the correlation between the prescriptions and the targets. Moreover, by emphasizing the KG relations when aggregating neighborhood information, the Relational Path network of the KGRN model has the ability to capture the relationship dependence in the KG path, which enhances the ability to predict the relationship between the prescriptions and the targets. The experiments under multiple configurations demonstrate that KGRN outperforms the related competitive benchmarks. © 2021, Springer Nature Switzerland AG.

关键词： Knowledge graph

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Funny words detection via Contrastive Representations and Pre-trained Language Model 2

Funny words detection via Contrastive Representations and Pr...

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2nd International Seminar on Artificial Intelligence, networking and information Technology, AINIT 2021

作者： Du, Yiming Tian, Zelin Key Lab of Computer Network and Information Integration Nanjing China University of British Columbia Dept. Computer Science Vancouver Canada

ISBN: (纸本)9781665412964

Funniness detection of news headlines is a challenging task in computational linguistics. However, most existing works on funniness detection mainly tackle the scenario by simply judging whether a sentence is humorous, whose result is unstable due to factors such as sentence length. To solve this issue, in this paper, our idea is to fine-grained mine the detailed information of the words and the contextual relationship between different words in the sentence, which help to evaluate the correlation between keywords and the funniness of news headlines quantitatively. Specifically, we propose a funny words detection algorithm based on the contrastive representations learning and BERT model. To quantify the impact of different words on the degree of humor, we first subtract the funniness grades of the original news headlines and the funniness grades of the original news headlines with a single word replaced. Both funniness grades are predicted with a pre-trained model, which is supervised by a a threshold to limit the amount of data and ensure the validity of data. To ensure the accuracy of our prediction, we further introduce the contrastive learning to constrain the differences of news headlines before and after word replacement. Finally, according to the Root Mean Square Error (RMSE) matrix in our experiment, we develop a BERT model with mixed sequence embedding to generate a table about words and their corresponding funniness improvement about the news headlines. © 2021 IEEE.

关键词： Mean square error

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Gradient sparsification for efficient wireless federated learning with differential privacy

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Science China(information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation School of Information and Communication Engineering Hainan University School of Communications and Information Engineering Nanjing University of Posts and Telecommunications School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collab.ratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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