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7th International Conference on Data Science and information Technology, DSIT 2024

作者： Ren, Shuai Liu, Yahui Zhu, Yaping Bing, Wanlong Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

Short video platforms have become an essential part of people's daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods. © 2024 IEEE.

关键词： Fake detection

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Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

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computer Systems Science & Engineering 2023年第5期45卷 1033-1045页

作者： Tianyu Liu Pengyu Liu Xiaowei Jia Shanji Chen Ying Ma Qian Gao The Information Department Beijing University of TechnologyBeijing 100124China School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810000China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing key Laboratory Beijing100124China Department of Computer Science University of Pittsburgh15260USA

Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target *** mitigate large number of parameters and improve the segmentation accuracy,we propose a new Squeeze-Depth-Wise UNet(SDW-UNet)deep learning model for sea-land remote sensing image *** proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules,which enhance the model capacity in combining multiple channels and reduces the model *** further explore the effect of position-encoded information in NLP(Natural Language Processing)domain on sea-land segmentation *** have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy,the number of parameters and the time cost for *** test results on remote sensing data sets of Guam,Okinawa,Taiwan China,San Diego,and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters,reduces prediction time cost and improves performance over other mainstream segmentation *** also show that the position encoding can further improve the accuracy of model segmentation.

关键词： Sea-land segmentation UNet depth-wise separable convolution squeeze-excitation position encoding

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data Science and information Technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

CoinSeg: Contrast Inter- and Intra- Class Representations fo...

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IEEE/CVF International Conference on computer Vision (ICCV)

作者： Zhang, Zekang Gao, Guangyu Jiao, Jianbo Liu, Chi Harold Wei, Yunchao Beijing Institute of Technology School of Computer Science China University of Birmingham School of Computer Science United Kingdom Beijing Jiaotong University WEI Lab Institute of Information Science China Beijing Key Laboratory of Advanced Information Science and Network China

ISBN: (纸本)9798350307184

Class incremental semantic segmentation aims to strike a balance between the models stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the models plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the models plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the models stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://***/zkzhang98/CoinSeg.

关键词： Training Adaptation models computer vision Semantic segmentation Gaussian distribution Stability analysis Proposals

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keyphrase Generation via Soft and Hard Semantic Corrections

Keyphrase Generation via Soft and Hard Semantic Corrections

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhao, Guangzhen Yin, Guoshun Yang, Peng Yao, Yu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

keyphrase generation aims to generate a set of condensed phrases given a source document. Although maximum likelihood estimation (MLE) based keyphrase generation methods have shown impressive performance, they suffer from the bias on the source-prediction pair and the bias on the prediction-target pair. To tackle the above biases, we propose a novel correction model CorrKG on top of the MLE pipeline, where the biases are corrected via the optimal transport (OT) and a frequency-based filtering-and-sorting (FreqFS) strategy. Specifically, OT is introduced as the soft correction to facilitate the alignment of salient information and rectify the semantic bias on the source document and predicted keyphrases pair. An adaptive semantic mass learning scheme is conducted on the vanilla OT to achieve a proper pair-wise optimal transport procedure, which promotes the OT calculation brought by rectifying semantic masses dynamically. Besides, the FreqFS strategy is designed as the hard correction to reduce the bias of predicted and target keyphrases, and thus generate accurate and sufficient keyphrases. Extensive experiments over multiple benchmark datasets show that our model achieves superior keyphrase generation as compared with the state-of-the-arts. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Intelligent Optimization Method for Mobile NOMA network using IMBO 6

Intelligent Optimization Method for Mobile NOMA Network usin...

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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Fu, Xingyue Xu, Lingwei Gao, Zhihe Chen, Zhe Li, Yufang Cao, Shubo Qingdao University of Science & Technology Qingdao China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing211189 China

ISBN: (纸本)9798350399059

Non-orthogonal multiple access (NOMA) technology can significantly increase access volume and spectrum efficiency. The introduction of NOMA technology into the mobile communication system can significantly enhance communication performance. The power allocation technology can greatly improve the performance of NOMA systems and has become a research hotspot. However, many research schemes currently have many problems, such as poor system performance and inability to balance between user fairness and service quality. Therefore, this article studies the intelligent optimization for mobile NOMA network using improved Monarch Butterfly Optimization Algorithm(IMBO). First, an N-Nakagami channel-based mobile cooperative NOMA multi-user network is established. Then, considering the non-ideal communication condition, namely imperfect channel state information (ICSI), the exact expressions of outage probability (OP) for the mobile cooperative NOMA multi-user communication system are derived, and the relation between user power allocation coefficient and the performance of OP is investigated. Finally, an optimization algorithm of power allocation based on the IMBO is put forward, which can get the optimal power allocation coefficient. Simulation results indicate that, compared to the monarch butterfly optimization algorithm(MBO), the artificial fish swarm algorithm(AFSA), and the firefly algorithm(FA), the IMBO is at least 30% more efficient, thereby enhancing the performance of the communication system. © 2023 IEEE.

关键词： Spectrum efficiency

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SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition 29

SEE-Few: Seed, Expand and Entail for Few-shot Named Entity R...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yang, Zeng Zhang, Linhai Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used. To tackle training-from-scratch setting, it is crucial to make full use of the annotation information (the boundaries and entity types). Therefore, in this paper, we propose a novel multi-task (Seed, Expand and Entail) learning framework, SEE-Few, for Few-shot NER without using source domain data. The seeding and expanding modules are responsible for providing as accurate candidate spans as possible for the entailing module. The entailing module reformulates span classification as a textual entailment task, leveraging both the contextual clues and entity type information. All the three modules share the same text encoder and are jointly learned. Experimental results on four benchmark datasets under the training-from-scratch setting show that the proposed method outperformed state-of-the-art few-shot NER methods with a large margin. Our code is available at https://***/unveiled-the-red-hat/SEE-Few. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Classification (of information)

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ABCD-HN: An Artificial network Benchmark for Community Detection on Heterogeneous networks 18th

ABCD-HN: An Artificial Network Benchmark for Community Dete...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Liu, Junjie Guo, Kun Wu, Ling Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial network Benchmark for Community Detection on Heterogeneous networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heterogeneous networks

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Complicate then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification 29

Complicate then Simplify: A Novel Way to Explore Pre-trained...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhang, Xu Liu, Zejie Xiang, Yanzheng Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

In the developing context of pre-trained models (PTMs), the performance of text classification has been continuously improved by directly employing the features generated by PTMs. However, such a way might not fully explore the knowledge in PTMs as it is constrained by the difficulty of the task. Compared to a difficult task, the learning algorithms tend to saturate early on the simple task. Moreover, the native sentence representations derived from BERT are prone to be collapsed and directly employing such representation for text classification might fail to fully capture discriminative features. In order to address these issues, in this paper we propose a novel framework for text classification which implements a two-stage training strategy. In the pre-training stage, auxiliary labels are introduced to increase the task difficulties and to fully exploit the knowledge in the pre-trained model. In the fine-tuning stage, the textual representation learned in the pre-training stage is employed and the classifier is fine-tuned to obtain better classification performance. Experiments were conducted on six text classification corpora and the results showed that the proposed framework outperformed several state-of-the-art baselines. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Classification (of information)

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Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding 29

Temporal Knowledge Graph Completion with Approximated Gaussi...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhang, Linhai Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Knowledge Graphs (KGs) stores world knowledge that benefits various reasoning-based applications. Due to their incompleteness, a fundamental task for KGs, which is known as Knowledge Graph Completion (KGC), is to perform link prediction and infer new facts based on the known facts. Recently, link prediction on the temporal KGs becomes an active research topic. Numerous Temporal Knowledge Graph Completion (TKGC) methods have been proposed by mapping the entities and relations in TKG to the high-dimensional representations. However, most existing TKGC methods are mainly based on deterministic vector embeddings, which are not flexible and expressive enough. In this paper, we propose a novel TKGC method, TKGC-AGP, by mapping the entities and relations in TKG to the approximations of multivariate Gaussian processes (MGPs). Equipped with the flexibility and capacity of MGP, the global trends as well as the local fluctuations in the TKGs can be simultaneously modeled. Moreover, the temporal uncertainties can be also captured with the kernel function and the covariance matrix of MGP. Moreover, a first-order Markov assumption-based training algorithm is proposed to effective optimize the proposed method. Experimental results show the effectiveness of the proposed approach on two real-world benchmark datasets compared with some state-of-the-art TKGC methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Knowledge graph

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