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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Kailin Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences China

Prototypical network based joint methods have attracted much attention in few-shot event detection, which carry out event detection in a unified sequence tagging framework. However, these methods suffer from the inaccurate prototype representation problem, due to two main reasons: the number of instances for calculating prototypes is limited;And, they do not well capture the relationships among event prototypes. To deal with this problem, we propose a Knowledge-Enhanced self-supervised Prototypical Network, called KE-PN, for few-shot event detection. KE-PN adopts hybrid rules, which can automatically align event types to an external knowledge base, i.e., FrameNet, to obtain more instances. It proposes a self-supervised learning method to filter out noisy data from enhanced instances. KE-PN is further equipped with an auxiliary event type relationship classification module, which injects the relationship information into representations of event prototypes. Extensive experiments on three benchmark datasets, i.e., FewEvent, MAVEN, and ACE2005 demonstrate the state-of-the-art performance of KE-PN. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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A Module Placement Method for Digital Microfluidic Biochips Based on Security Design 23

A Module Placement Method for Digital Microfluidic Biochips ...

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Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Zhongliao Yang Yinan Yao Xiaodong Guo Peixin Huang Jiayin Li Yuzhong Chen Xintian Liang College of Computer and Data Science Fuzhou University China College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

Digital Microfluidic Biochips (DMFBs) represent a miniaturized, integrated, high-precision, and cost-effective platform with widespread applications in fields such as mobile healthcare. Nevertheless, DMFBs face increasingly intricate advanced synthesis challenges. Furthermore, the inclusion of unreliable third-party elements in the chip manufacturing process and the implantation of hardware trojans can lead to erroneous measurement results, potentially resulting in incorrect diagnostic approaches. This paper introduces a novel genetic algorithm encoding approach to address this issue, allowing simultaneous consideration of operation prioritization, module selection, and module placement. Additionally, it defines three different hierarchical queues and employs distinct module selection strategies for each queue, enhancing operational scheduling efficiency and achieving higher spatial utilization in module placement. This approach outperforms traditional module placement algorithms, resulting in a 8.7%-34% improvement in execution time. Simultaneously, to tackle the issue of incorrect results caused by the insertion of malicious hardware trojans, the method employs electrode tagging to lower the cost of DMFB utilization while ensuring security. Experimental results validate the effectiveness and security of this approach, providing an essential foundation for trustworthy advanced synthesis design in DMFBs.

关键词： Digital Microfluidic Biochips genetic algorithm module placement security

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Sparse PCA: Phase Transitions in the Critical Sparsity Regime

arXiv

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arXiv 2024年

作者： Feldman, Michael J. Misiakiewicz, Theodor Romanov, Elad Faculty of Mathematics and Computer Science Weizmann Institute of Science Department of Statistics and Data Science Yale University United States Department of Statistics Stanford University United States

This work studies estimation of sparse principal components in high dimensions. Specifically, we consider a class of estimators based on kernel PCA, generalizing the covariance thresholding algorithm proposed by Krauthgamer et al. (2015). Focusing on Johnstone’s spiked covariance model, we investigate the "critical" sparsity regime, where the sparsity level m, sample size n, and dimension p each diverge and m/√n → β, p/n → γ. Within this framework, we develop a fine-grained understanding of signal detection and recovery. Our results establish a detectability phase transition, analogous to the Baik–Ben Arous–Péché (BBP) transition: above a certain threshold—depending on the kernel function, γ, and β—kernel PCA is informative. Conversely, below the threshold, kernel principal components are asymptotically orthogonal to the signal. Notably, above this detection threshold, we find that consistent support recovery is possible with high probability. Sparsity plays a key role in our analysis, and results in more nuanced phenomena than in related studies of kernel PCA with delocalized (dense) components. Finally, we identify optimal kernel functions for detection—and consequently, support recovery—and numerical calculations suggest that soft thresholding is nearly optimal. © 2024, CC BY.

关键词： Optimal detection

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High-Order Resting-State Functional Connectivity is Predictive of Working Memory Decline After Brain Tumor Resection

High-Order Resting-State Functional Connectivity is Predicti...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Triet M. Tran Thi T. Tran Sina Khanmohammadi Data Science And Analytics Institute University of Oklahoma Norman OK USA School of Computer Science University of Oklahoma Norman OK USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients.

关键词： Surgery Functional magnetic resonance imaging Planning Regression analysis Neurosurgery Engineering in medicine and biology Monitoring

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Depression Detection using Extreme Learning Machine

Depression Detection using Extreme Learning Machine

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Prajna Dutta Deepak Gupta Jyoti Maurya School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM) Vithura India Computer Science and Engineering Department MNNIT Allahabad Prayagraj India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Depression is the most well-known type of physiological or mental health problem that affects a large number of people globally. Extreme learning machine (ELM) techniques are currently preferred to solve wide range of health disease detection and prediction issues. ELM is a single hidden layer feed-forward neural network (SLFN), which converges much faster than the other traditional Machine Learning (ML) methods and yields promising results. Many research works already exist on the application of Machine Learning (ML) models to the Depression Detection dataset but little to no work was found wherein Extreme Learning Machine was used for Depression Detection. This research work has applied Extreme Learning Machine (ELM) and other ML techniques for depression detection and compared the results obtained and found that, ELM has delivered the best performance with an accuracy of 91.73%.

关键词： Surveys Pervasive computing Neurological diseases Accuracy Extreme learning machines Social networking (online) Prevention and mitigation

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Fast Sampling of Diffusion Models for Accelerated MRI using Dual Manifold Constraints

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qiao, Lihong Wang, Rongxuan Shu, Yucheng Li, Baobin Li, Weisheng Gao, Xinbo Cai, Zhanchuan Chongqing University of Posts and Telecom munications Chongqing Big Data Collaborative Innovation Center Chongqing400065 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Macau University of Science and Technology School of Computer Science and Engineering Macau China

Diffusion models show great potential in solving inverse problems, including MRI reconstruction. With its unique characteristics, medical imaging demands both efficiency and accuracy in the reconstruction process. However, existing MRI reconstruction methods based on diffusion models often fall short of fully leveraging the available measurements during sampling. Consequently, these methods suffer from compromised reconstruction quality and elevated bias, especially when dealing with large acceleration factors. In response to these challenges, we propose Dual Manifold Constraints (DMC), a fast MRI reconstruction method based on diffusion models. We treat the sampling process as a combination of denoising and adding noise processes, and we constrain these two processes using both pristine measurements and their noisy counterparts to adapt to the geometry of diffusion. It's worth noting that we propose a method to estimate the noisy measurement that satisfies the sub-sampling process to maintain the current data manifold when performing data consistency constraints. Experimental results show that our method outperforms the latest diffusion-based methods regarding both reconstruction speed and accuracy, and exhibits strong out-of-distribution generalization performance. © 1991-2012 IEEE.

关键词： Inverse problems

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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The Effectiveness of Different Deep Learning Models in Detecting Hate Speech on Social Media

The Effectiveness of Different Deep Learning Models in Detec...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Tahsin Zaman Jilan Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Recent changes in social media made it harder to control the propagation of hate speech. One potential solution can be use of deep learning models for automated hate speech recognition. In this work, we evaluate how well different deep learning models classify hate speech on social networks. In our experiments, we use a dataset of social media posts with and without hate *** examine the results of a number of different models, including attention-based models, convolutional neural networks (CNNs), and long short-term memory (LSTM). We also examine the effects of additional variables, such as the amount of training data and the use of pre-trained word embeddings, on the performance of these models. Our results demonstrate that attention-based models perform better than CNN and LSTM algorithms in identifying hate speech. To sum up, our research offers valuable perspectives on enhancing deep learning models for the identification of hate speech.

关键词： Deep learning Social networking (online) Computational modeling Hate speech Training data Speech recognition Tokenization data models Convolutional neural networks Long short term memory

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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CREAM: Named Entity Recognition with Concise query and REgion-Aware Minimization 24th

CREAM: Named Entity Recognition with Concise query and REg...

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Yao, Xun Yang, Qihang Hu, Xinrong Yang, Jie Guo, Yi School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia

ISBN: (纸本)9789819972531

Recent advancements in Machine Reading Comprehension (MRC) models have sparked interest in the field of Named Entity Recognition (NER), where entities are extracted as answers of given queries. Yet, existing MRC-based models face several challenges, including high computational costs, limited consideration of entity content information, and the tendency to generate sharp boundaries, that hinder their generalizability. To alleviate these issues, this paper introduces CREAM, an enhanced model leveraging Concise query and REgion-Aware Minimization. First, we propose a simple yet effective strategy of generating concise queries based primarily on entity categories. Second, we propose to go beyond existing methods by identifying entire entities, instead of just their boundaries (start and end positions), with an efficient continuous cross-entropy loss. An in-depth analysis is further provided to reveal their benefit. The proposed method is evaluated on six well-known NER benchmarks. Experimental results demonstrate its remarkable effectiveness by surpassing the current state-of-the-art models, with the substantial averaged improvement of 2.74, 1.12, and 2.38 absolute percentage points in Precision, Recall, and F1 metrics, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine Reading Comprehension Named Entity Recognition Query Optimization Region-Aware Loss

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