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29th International Conference on Computational Linguistics, COLING 2022

作者： Gui, Xiangyu Zhao, Feng Jin, Langjunqing Jin, Hai 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 China

Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector or a point in a feature space. The distance between these points is a measure of semantic similarity. However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others. It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities. To alleviate the semantic ambiguity in KGs, we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference. It is a lightweight and relation-adaptive model based on coherence theory, in which each entity’s semantics vary automatically regarding different relations. In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process. The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Multi-level Distance Metric Learning for Few-shot Spoken term classification 7

Multi-level Distance Metric Learning for Few-shot Spoken ter...

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2022 7th International Conference on Electronic Technology and Information science, ICETIS 2022

作者： Wu, Yiqi Ma, Zhongchen Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212000 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Zhenjiang212000 China

ISBN: (纸本)9783800757794

Spoken term classification has attracted great attention of researchers because of its wide application in real life. Traditional methods use a deep neural network trained with tremendous samples to classify a particular keyword which may not be applicable in case of insufficient samples and restrict the system from recognizing new or user-defined keywords. Few-shot learning algorithm is then applied to this speech task since it can use only a few examples to adapt to classifying new classes. Metric learning for few-shot learning aims to learn a metric in episode learning such that the distance between the positive pair of each episode is closed and the distance between the negative pair of each episode is far away. However, due to various pronunciation, speed or intonation, some spoken samples may be extremely dissimilar to its positive sample, but very similar to its negative sample, leading these samples to be ignored or overly influenced in metric learning. Therefore, to cope with this problem we propose a multi-level distance regularized metric learning method for few-shot spoken term classification. It explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels. In this way, the degree of similarity between a pair can be fully utilized and the importance of each sample in metric learning is considered in a balanced manner. Multi-level Distance Regularization with simple Prototypical Network achieves the state-of-the-art performance in benchmark dataset: Google Speech Commands dataset. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Learning algorithms

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Aluminum Scandium Nitride Lamb Wave Acoustic Delay Lines with Over 6% Fractional Bandwidth 22

Aluminum Scandium Nitride Lamb Wave Acoustic Delay Lines wit...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Luo, Zhifang Shao, Shuai Lee, Chengkuo Wu, Tao School Of Information Science And Technology ShanghaiTech University China Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences China University Of Chinese Academy Of Sciences China Shanghai Engineering Research Center Of Energy Efficient And Custom Ai Ic China Department Of Electrical & Computer Engineering National University Of Singapore Singapore Center For Intelligent Sensors And Mems National University Of Singapore Singapore

ISBN: (纸本)9784886864352

We report the low loss and broad fractional bandwidth (FBW) Acoustic Delay Lines (ADLs) based on 16% Sc-doped Aluminum Scandium Nitride (Al0.84Sc0.16N) Thin film. The fabricated ADLs show 3-dB FBW of 6.3% and minimum insertion loss (IL) of 5.6 dB at approximately 808 MHz. The ADLs with delay ranges from 20 ns to 41 ns have been demonstrated. A high group velocity of 9116 m/s is extracted utilizing fundamental symmetric (S0) mode Lamb wave. The simultaneous high-speed and broadband illustrate the great potential of AlScN ADLs for signal processing in the future. © 2023 IEEJ.

关键词： Acoustic delay lines AlScN SPUDT

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Revisiting Kernel Attention with Correlated Gaussian Process Representation

arXiv

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arXiv 2025年

作者： Bui, Long Minh Huu, Tho Tran Dinh, Duy Nguyen, Tan Minh Hoang, Trong Nghia FPT Software AI Center Viet Nam Department of Mathematics National University of Singapore Singapore School of Electrical Engineering and Computer Science Washington State University United States

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP’s kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks. The code for our experiments is available at https://***/MinhLong210/CGP-Transformers. Copyright © 2025, The Authors. All rights reserved.

关键词： Global positioning system

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A User Behaviour-Based Video Segmentation Strategy for High-Concurrency Environment

A User Behaviour-Based Video Segmentation Strategy for High-...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Danning Shen Wenhao Zhu Yujie Ding Chenyun Liu School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Municipal Big Data Center Shanghai China

With the development of the media industry, maximizing concurrency while ensuring the user’s viewing experience has become an important research topic for each major video website. Currently, one solution is to slice the video, which conserves server bandwidth. Therefore, the design of segment durations has been widely discussed in academia. The research shows that the longer the segment duration is, the better the user experience; however, the increase in video segment duration has a negative impact on the concurrency of the video website. In this paper, a variable length segmentation strategy based on user behaviour is proposed for video segmentation. In the early stage of video browsing, the video loading is ensured, and a short video segmentation strategy is adopted. When the number of users becomes stable, the segment duration is slowly increased to a fixed value to ensure fluency for the users watching the videos. In this paper, the JMeter test tool is used to test the concurrency volume. Through experimental verification, the variable length video segmentation algorithm proposed in this paper is shown to effectively improve the concurrency of the video websites.

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Improving multi-view document clustering: leveraging multi-structure processor and hybrid ensemble clustering module 30

Improving multi-view document clustering: leveraging multi-s...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bai, Ruina Bai, Qi Huang, Ruizhang Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Mathematical Sciences Zhejiang University Hangzhou310058 China

ISBN: (纸本)9782493814104

We introduce a multi-view document clustering model called DMsECN (Deep Multi-structure Ensemble Clustering Network), comprising a multi-structure processor and a hybrid ensemble clustering module. Unlike existing models, DMsECN distinguishes itself by creating a consensus structure from multiple clustering structures. The multi-structure processor comprises two stages, each contributing to the extraction of clustering structures that preserve both consistency and complementarity across multiple views. Representation learning extracts both view and view-fused representations from multi-views through the use of contrastive learning. Subsequently, multi-structure learning employs distinct view clustering guidance to generate the corresponding clustering structures. The hybrid ensemble clustering module merges two ensemble methods to amalgamate multiple structures, producing a consensus structure that guarantees both the separability and compactness of clusters within the clustering results. The attention-based ensemble primarily concentrates on learning the contribution weights of diverse clustering structures, while the similarity-based ensemble employs cluster assignment similarity and cluster classification dissimilarity to guide the refinement of the consensus structure. Experimental results demonstrate that DMsECN outperforms other models, achieving new state-of-the-art results on four multi-view document clustering datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Structure (composition)

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An accurate cell segmentation method for high-resolution complex brain image

An accurate cell segmentation method for high-resolution com...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Xiaofeng Xu Shuai Li Wei Zheng Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Cell segmentation is a key step in medical image analysis and cell biology research, and is important in areas such as understanding cell behaviour, cancer detection and cell cycle analysis. This task is challenging due to the presence of overlap between cells and image noise. With the development of imaging techniques, the amount and quality of acquired cellular image data have increased significantly and even contain diverse information such as neuronal structures and blood vessels, which puts higher demands on segmentation techniques. Real rat brain cell images obtained using micro-optical slice tomography (MOST) contain complex and diverse information such as neuronal structure, cell morphology, and the distribution of blood vessels, demonstrating the characteristics of real biological samples, for which segmentation of the target region is more difficult. In order to solve these challenges, this paper proposes a new cell image segmentation method that combines watershed order differential feature extraction and fuzzy entropy spatial theory. Due to the complexity of image information, we first use traditional image enhancement techniques. Then feature extraction is performed and fuzzy entropy processing is added to achieve accurate identification of cell boundaries. Then distance transform and curvature analysis are added to further segment the cell clusters into individual cells while avoiding over-segmentation and missed detection. Experiments demonstrate that the method not only shows high performance on our dataset, but also validates our method on several publicly available cellular datasets respectively, proving that the segmentation algorithm shows great potential for segmentation on different types of cells. Future research will further optimise the model to improve segmentation performance on complex and irregularly shaped cells and explore the application of these methods to other types of medical image segmentation tasks.

关键词： Image segmentation Accuracy Morphology Clustering algorithms Transforms Feature extraction Entropy Complexity theory data mining Biomedical imaging

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Decomposed linear dynamical systems (dLDS) for learning the latent components of neural dynamics

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 2962-2973页

作者： Noga Mudrik Yenho Chen Eva Yezerets Christopher J. Rozell Adam S. Charles Department of Biomedical Engineering Center for Imaging Science Kavli NDI Johns Hopkins University Baltimore MD Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA Department of Biomedical Engineering Center for Imaging Science Johns Hopkins University Baltimore MD School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA Department of Biomedical Engineering Center for Imaging Science Mathematical Institute for Data Science Kavli NDI Johns Hopkins University Baltimore MD

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents complex non-stationary and nonlinear dynamics of time series data as a sparse combination of simpler, more interpretable components. Our model is trained through a dictionary learning procedure, where we leverage recent results in tracking sparse vectors over time. The decomposed nature of the dynamics is more expressive than previous switched approaches for a given number of parameters and enables modeling of overlapping and non-stationary dynamics. In both continuous-time and discrete-time instructional examples, we demonstrate that our model effectively approximates the original system, learns efficient representations, and captures smooth transitions between dynamical modes. Furthermore, we highlight our model's ability to efficiently capture and demix population dynamics generated from multiple independent subnetworks, a task that is computationally impractical for switched models. Finally, we apply our model to neural "full brain" recordings of C. elegans data, illustrating a diversity of dynamics that is obscured when classified into discrete states.

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Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

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Journal of Integrative Agriculture 2023年第9期22卷 2865-2881页

作者： ZHANG Sha YANG Shan-shan WANG Jing-wen WU Xi-fang Malak HENCHIRI Tehseen JAVED ZHANG Jia-hua Bai Yun Research Center for Space Information and Big Earth Data College of Computer Science and TechnologyQingdao UniversityQingdao 266071P.R.China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094P.R.China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000P.R.China Department of Environmental Sciences Kohat University of Science and TechnologyKohat 26000Pakistan

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food ***,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited ***,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing *** approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and ***,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the *** IPPs were determined using statistical yield data of reference years(2010–2015)over the *** findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),*** data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–*** addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city ***,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

关键词： approximating irrigations process-based model remote sensing winter wheat yield North China Plain

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