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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Xu, Jianhan Li, Linyang Zhang, Jiping Zheng, Xiaoqing Chang, Kai-Wei Hsieh, Cho-Jui Huang, Xuanjing School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing China Department of Computer Science University of California Los Angeles United States

The existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. Many methods have been developed to defend against adversarial attacks for neural natural language processing (NLP) models. Adversarial training is one of the most successful defense methods by adding some random or intentional perturbations to the original input texts and making the models robust to the perturbed examples. In this study, we explore the feasibility of improving the adversarial robustness of NLP models by performing perturbations in the parameter space rather than the input feature space. The weight perturbation helps to find a better solution (i.e., the values of weights) that minimizes the adversarial loss among other feasible solutions. We found that the weight perturbation can significantly improve the robustness of NLP models when it is combined with the perturbation in the input embedding space, yielding the highest accuracy on both clean and adversarial examples across different datasets. © 2022 Association for Computational Linguistics.

关键词： Natural language processing systems

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Pseudo-3D CNN with inter-slice attention for glioma grading 3

Pseudo-3D CNN with inter-slice attention for glioma grading

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3rd International Conference on computer science and Communication technology, ICCSCT 2022

作者： Du, Shanshan Cao, Zhuoqun Zhao, Rui-Wei Zhang, Xiaobo Feng, Rui School of Information Science and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Children's Hospital Fudan University Shanghai China

ISBN: (纸本)9781510661240

Gliomas comprise around 80 percent of all malignant brain tumors which can be further classified into the low-grade glioma and high-grade glioma categories. Compared to low-grade gliomas, high-grade gliomas show more malignant behaviour since they usually grow rapidly and frequently destroy healthy brain tissue. Therefore, it is important to determine the malignancy of gliomas for initial treatment plan. Most existing methods are based on radiomics or transfer learning which extract features from single-slice without considering that 3D features between adjacent slices can provide stronger discriminative power. In this work, we propose to incorporate the attention mechanism and pseudo-3d module into a deep convolutional network architecture, which can reduce model size and produce fine features for glioma grading. Our work focuses on the inter-slice relationship and propose an attention unit, named "Inter-Slice Attention Module", which adaptively refines intermediate feature maps by modelling dependencies between adjacent slices. We evaluate our method on an open dataset of gliomas, achieving mean accuracy of 89.47%. © 2022 SPIE.

关键词： Grading

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Distributed Multi-Agent Resource Allocation Based on Transformer and DRL for Cloud XR Hybrid Content Transmission

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IEEE Internet of Things Journal 2025年第12期12卷 22110-22127页

作者： Wang, Zhaocheng Wu, Jun Wang, Rui Li, Ying Tongji University School of Computer Science and Technology Shanghai201804 China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tongji University College of Electronics and Information Engineering Shanghai201804 China Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China Peng Cheng Laboratory Guangdong518055 China

Extended reality (XR) technologies and applications have grown rapidly in recent years. In addition to providing immersive ultra-high-definition (UHD) video, XR also allows for a haptic experience where devices can be remotely manipulated to accomplish tasks. However, varying numbers of XR users accessing the communication system can strain limited spectrum resources, posing challenges in resource allocation. Therefore, this paper studies resource blocks (RBs) allocation problem in a downlink transmission scenario where real-time cloud XR video and haptic contents need to be transmitted simultaneously. We also consider the random variation in the number of XR users and propose an Adaptive Distributed Multi-Agent Deep reinforcement learning (DRL) combined with Transformer (ADMA-DcT) for dynamic RB allocation method. This method addresses the dynamic change in network input dimensions due to user number variability using a state division module and a self-attention mechanism in the encoder module. To our knowledge, this is the first work to study the RB allocation problem in Cloud XR transmission considering simultaneous transmitting of video and haptic services with a dynamically changing user base. Our extensive simulations show that the ADMA-DcT model, end-to-end trained, outperforms other benchmarks in successfully serving a larger number of XR users under varying user number conditions, demonstrating excellent adaptivity and robustness. © 2014 IEEE.

关键词： Multi agent systems

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection 24

HD-KT: Advancing Robust Knowledge Tracing via Anomalous Lear...

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33rd ACM Web Conference, WWW 2024

作者： Ma, Haiping Yang, Yong Qin, Chuan Yu, Xiaoshan Yang, Shangshang Zhang, Xingyi Zhu, Hengshu Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Beijing China School of Artificial Intelligence Anhui University Anhui Hefei China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin Beijing China

ISBN: (纸本)9798400701719

Knowledge tracing (KT) is a crucial task in online learning, aimed at tracing and predicting each student's knowledge states throughout their learning process. Over the past decade, it has garnered widespread attention due to it provides the potential for more tailored and adaptive online learning experiences. Although most current KT methodologies emphasize optimizing network structures to enhance predictive accuracy for future student performance, they often neglect anomalous interactions in students' learning processes, which may arise from low data quality (i.e., inferior question quality) and abnormal student behaviors (i.e., guessing and mistakes). To this end, in this paper, we propose a novel framework, termed HD-KT, designed to enhance the robustness of existing KT methodologies with Hybrid learning interactions Denoising approach. Specifically, we introduce two detectors for anomalous learning interactions, namely knowledge state-guided anomaly detector and student profile-guided anomaly detector. In the first detection module, we design a sequential autoencoder to identify anomalous learning interactions by detecting atypical student knowledge states. In the second module, we incorporate an attention mechanism by modeling a student's long-term profile to capture irregular interactions. Extensive experiments on four real-world benchmark datasets have decisively shown our HD-KT markedly boosts the robustness of numerous prevailing KT models, consequently increasing the accuracy of future student performance predictions. Additionally, our case studies highlight the versatility of HD-KT in addressing diverse downstream tasks, such as exercise quality analysis and learning behavior-based student clustering. © 2024 ACM.

关键词： Students

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HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 976-990页

作者： Yuliang Pan Ruiyi Li Wengen Li Liuzhenghao Lv Jihong Guan Shuigeng Zhou Department of Computer Science and Technology College of Electronic and Information EngineeringTongji UniversityShanghai 201804China Translational Medical Center for Stem Cell Therapy Shanghai East HospitalSchool of MedicineTongji UniversityShanghai 200120China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of *** a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein ***,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological *** fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to *** integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized *** with the existing methods,our method achieved outstanding performance on both testing and independent ***,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting *** results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological *** HPCAtlas webserver is freely available at http://***/HPC-Atlas.

关键词： Human protein complex Protein interaction network SARS-CoV-2-affected complex Multifunctional protein Complex identification method

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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

作者： Yangdong Chen Mohan Chen Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research.

关键词： Point cloud compression Three-dimensional displays Image synthesis Superresolution Contrastive learning Benchmark testing Signal processing Diffusion models Stability analysis Speech processing

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VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition 27

VANER: Leveraging Large Language Model for Versatile and Ada...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

ISBN: (纸本)9781643685489

The prevalent solution for BioNER involves using representation learning techniques combined with sequence ***, such methods are inherently task-specific, demonstrate poor generalizability, and often require a dedicated model for each *** leverage the versatile capabilities of recent large language models (LLMs), several approaches have explored generative techniques for entity ***, these approaches often fall short compared to previous sequence labeling *** this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and *** combining the LLM's understanding of instructions with sequence labeling techniques, we train a model using a mix of datasets capable of extracting various types of *** that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to enable the model to densely recognize curated *** parameter-efficient training model, VANER, significantly outperforms previous LLMs-based *** the first time, as an LLM-based model, VANER surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. © 2024 The Authors.

关键词： labeled data

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KCE-Unet: A novel music denoising method with KANConv ECA Unet*

KCE-Unet: A novel music denoising method with KANConv ECA Un...

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

作者： Shijie Zhang Yulun Wu Ganghui Ru Yi Yu Wei Li School of Computer Science and Technology Fudan University Shanghai China Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan School of Computer Science and Technology Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

During concerts, people often spontaneously record memorable moments with their phones. However, these recordings are frequently accompanied by noise, such as cheering and applause, which diminishes the playback experience. In this paper, we introduce a novel task specifically designed for denoising music in concert environments, a challenge that has been largely overlooked in previous research. To support this task, we created a new concert denoising dataset that includes songs performed in various major languages at concerts, with noise segments like cheering and applause. Building on this, we propose KANConv ECA Unet (KCE-Unet), a method that combines the U-Net network, efficient channel attention (ECA), and the recently proposed KAN network to flexibly remove noise in the mid-to-high frequency range of spectrograms. Extensive experiments demonstrate that our method outperforms previous models in denoising performance and effectively restore disrupted musical structures.

关键词： Noise reduction Noise Buildings Acoustics Recording Multiple signal classification Speech processing Spectrogram

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Minimizing Disparities between Real and Pseudo Queries for Unsupervised Visual Grounding

Minimizing Disparities between Real and Pseudo Queries for U...

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

作者： Hui Jiang Changkai Ji Jilan Xu Yanhao Zhu Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Visual grounding involves the identification and localization of image regions given textual descriptions. To reduce the manual labeling effort on region-text pairs, unsupervised visual grounding aims to generate pseudo bounding box and query pairs for training grounding models. However, there exists significant disparities between real and pseudo queries in terms of object, attribute distributions, and textual formats, limiting the generalization performance of unsupervised grounding methods. To address this challenge, we propose a novel unsupervised visual grounding framework. During training, we prompt Multimodal Large Language Models to generate pseudo queries, in which the entities are beyond the object detector’s pre-defined limited categories, and are associated with richer attributes. We further devise a Modifier Tree structure to bridge the gap of textual format between real and pseudo queries. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art unsupervised approaches on public benchmark datasets, particularly when dealing with complex queries.

关键词： Training Location awareness Visualization Limiting Grounding Large language models Manuals labeling Speech processing Image reconstruction

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