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arXiv 2024年

作者： Wang, Yan Zhou, Jie Fan, Xing-Yan Hao, Ze-Yan Li, Jia-Kun Liu, Zheng-Hao Sun, Kai Xu, Jin-Shi Chen, Jing-Ling Li, Chuan-Feng Guo, Guang-Can CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China School of Physics Hangzhou Normal University Hangzhou310036 China College of Physics and Materials Science Tianjin Normal University Tianjin300382 China Theoretical Physics Division Chern Institute of Mathematics Nankai University Tianjin300071 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

As the fundamental tool in quantum information science, the uncertainty principle is essential for manifesting nonclassical properties of quantum systems. Plenty of efforts on the uncertainty principle with two observables have been achieved, making it an appealing challenge to extend the scenario to multiple observables. Here, based on an optical setup, we demonstrate the uncertainty relations in two-qubit systems involving three physical components with the tight constant 2/√3, which signifies a more precise limit in the measurement of multiple quantum components and offers deeper insights into the trade-offs between observables. Furthermore, we reveal the correspondence of the maximal values of the uncertainty functions and the degree of entanglement, where the more uncertainty is proportional to the higher degree of entanglement. Our results provide a new insight into understanding the uncertainty relations with multiple observables and may motivate more innovative applications in quantum information science. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Unveiling the corrosion evolution and pitting mechanism of X70 pipeline steel in typical near-neutral/bentonite soil environment

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Journal of Materials science & technology 2026年 243卷 149-166页

作者： Guangming Yang Lulu Zeng Hua Shen Huaiyun Cui Tianqi Chen Zengyao Chen Chengwei Xu Chao Liu Feng Huang Zhiyong Liu Zhichao Che Cuiwei Du Xiaogang Li Key Laboratory for Corrosion and Protection (MOE) Institute of Advanced Materials and Technology University of Science and Technology Beijing Beijing 100083 China National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing Beijing 100083 China Jilin Oilfield Company China National Petroleum Corporation Songyuan 138000 China Corrosion and Control Research Center Branch of Science and Technology Research Institute of National Oil and Gas Pipeline Network Group Co Tianjin 300457 China The State Key Laboratory of Refractories and Metallurgy Wuhan University of Science and Technology Wuhan 430081 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing 100083 China

This work investigated the effect of dissolved oxygen (DO) on the corrosion behavior and pitting mechanism of X70 pipeline steel in near-neutral soil-bentonite simulated environment. The localized corrosion evolution to uniform corrosion was quantitatively analyzed using statistical methods. In situ characterization reveals that the localized corrosion mechanism is induced by core-shell inclusions. The microcracks occur between MnS and core oxides, the galvanic effect exists between MnS and matrix, and the high distortion region of the matrix promotes the localized corrosion initiation, while the galvanic effect between ferrite and pearlite aids propagation. Interestingly, the pitting growth rates are higher at low DO concentrations. Furthermore, the lower DO and longer immersion time enhance rust layer protection and reduce the corrosion rate of X70 pipeline steel.

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PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models

arXiv

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arXiv 2022年

作者： Wu, Chen Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China University of Amsterdam Netherlands

Neural ranking models (NRMs) have shown remarkable success in recent years, especially with pre-trained language models. However, deep neural models are notorious for their vulnerability to adversarial examples. Adversarial attacks may become a new type of web spamming technique given our increased reliance on neural information retrieval models. Therefore, it is important to study potential adversarial attacks to identify vulnerabilities of NRMs before they are deployed. In this paper, we introduce the Word Substitution Ranking Attack (WSRA) task against NRMs, which aims to promote a target document in rankings by adding adversarial perturbations to its text. We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list. This attack setting is realistic in real-world search engines. We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients for finding the adversarial perturbations. Experiments on two web search benchmark datasets show that PRADA can outperform existing attack strategies and successfully fool the NRM with small indiscernible perturbations of text. Copyright © 2022, The Authors. All rights reserved.

关键词： Search engines

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Malicious HTTPS Traffic Classification Algorithm Based on DCGAN_1D-CNN

Malicious HTTPS Traffic Classification Algorithm Based on DC...

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2021 IEEE Conference on Telecommunications, Optics and Computer science, TOCS 2021

作者： Luo, Wen Liu, Zhaohui Zhao, Rui Chen, Jianhua Deng, Xin University of South China School of Computer Science Hengyang China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9781665424981

Aiming at the problems of fast classification and unbalanced data classification of encrypted malicious traffic in the internet, a classification method of encrypted malicious based on DCGAN 1D-CNN model is ***-IDCNN uses the idea of generating confrontation to generate a few samples in the data level to improve the lack of original training samples and sample imbalances,and use the ID-CNN to train the original sample and the generated few samples,so as to accurately classify the encrypted *** experimental results on the public dataset CICAndMal2017 show that the F1 value reaches 96.55% in the binary classification experiment, which is at most higher 11.77% than that of using 1D-CNN model alone,indicating that the DCGAN 1D-CNN model has stronger ability to classify encrypted malicious traffic. © 2021 IEEE.

关键词： Generative adversarial networks

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction Information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval

Learning Fine-Grained Information Alignment for Calibrated C...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Jianhua Dong Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Masked Language Modeling (MLM) and Image-Text Matching (ITM) are always used in fusion encoder to learn the joint representation of images and text. In existing methods, the masking strategy of MLM leads to the neglect of image details during the modeling process. Meanwhile, the sampling strategy of ITM struggles to consistently select high-difficulty hard negative instances, reducing the effectiveness of constraints. This leads to challenges in aligning fine-grained information in cross-modal retrieval. In response to this challenge, a fine-grained information alignment-based visual language model (FAM) is proposed in this paper. On one hand, the attribute-based masking strategy is employed in MLM, helping the model focus on the details of objects in images during modeling. On the other hand, the robust hard negative sample generation strategy provides challenging negative samples for ITM by altering the relationships between objects. This enables the model to align relationships between objects in different modalities and thus calibrates cross-modal retrieval. Extensive experiments demonstrate the effectiveness of the model in cross-modal retrieval tasks.

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MCAF: Multi-Headed Causal Attention Fusion Approach for Multimodal Sentiment Analysis

MCAF: Multi-Headed Causal Attention Fusion Approach for Mult...

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International Joint Conference on Neural networks (IJCNN)

作者： Qingao Wang Xingang Wang Jinxiao Yang Yurun Bi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multimodal sentiment analysis(MSA) aims to synthesize multiple pieces of information, including textual, visual, and auditory information, to more accurately infer sentiment polarity. Due to the existence of complex and diverse affective causal relationships between different modalities, they interact with each other and constitute a multidimensional expression of human emotions. In the face of unbalanced data, i.e., when there are significant differences in the number of samples between different categories, the attention mechanism of the model tends to focus on spurious correlations in the training data, seriously impairing the model’s generalization ability and leading to bias, unbalanced prediction, and false correlation identification problems. To address the above problems, this study proposes a new multimodal fusion method based on the "within-sample sampling" and "cross-sample sampling" front door adjustment methods in causal inference strategies: the multi-head causal attention fusion method (MCAF). This method aims to capture potential causal relationships between different modalities through different attention heads, helping the model focus more on key causal features and avoid paying too much attention to false correlations in the training data. This helps improve the generalization performance of the model, make the decision-making process of the model more interpretable, and enhance the accuracy and robustness of the model for multimodal sentiment analysis tasks, making it more suitable for practical application scenarios.

关键词： Analytical models Sentiment analysis Visualization Correlation Neural networks Training data Predictive models

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Topic-based Multi-layer Knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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A Discriminative Semantic Ranker for Question Retrieval 21

A Discriminative Semantic Ranker for Question Retrieval

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11th ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2021

作者： Cai, Yinqiong Fan, Yixing Guo, Jiafeng Zhang, Ruqing Lan, Yanyan Cheng, Xueqi University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute for Ai Industry Research Tsinghua University Beijing China

ISBN: (纸本)9781450386111

Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typically implemented as multi-stage rankers: The first-stage ranker aims to recall potentially relevant questions from a large repository, and the latter stages attempt to re-rank the retrieved results. Most existing works on question retrieval mainly focused on the re-ranking stages, leaving the first-stage ranker to some traditional term-based methods. However, term-based methods often suffer from the vocabulary mismatch problem, especially on short texts, which may block the re-rankers from relevant questions at the very beginning. An alternative is to employ embedding-based methods for the first-stage ranker, which compress texts into dense vectors to enhance the semantic matching. However, these methods often lose the discriminative power as term-based methods, thus introduce noise during retrieval and hurt the recall performance. In this work, we aim to tackle the dilemma of the first-stage ranker, and propose a discriminative semantic ranker, namely DenseTrans, for high-recall retrieval. Specifically, DenseTrans is a densely connected Transformer, which learns semantic embeddings for texts based on Transformer layers. Meanwhile, DenseTrans promotes low-level features through dense connections to keep the discriminative power of the learned representations. DenseTrans is inspired by DenseNet in computer vision (CV), but poses a new way to use the dense connectivity which is totally different from its original design purpose. Experimental results over two question retrieval benchmark datasets show that our model can obtain significant gain on recall against strong term-based methods as well as state-of-the-art embedding-based methods. © 2021 ACM.

关键词： Semantics

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HGTA: News Recommendation Based on Hierarchical Granular Semantic Embeddings and Threshold Attention

HGTA: News Recommendation Based on Hierarchical Granular Sem...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Zhijun Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Personalized news recommendation helps users find interesting content among much news information. Most existing news recommendation techniques densely interact a user’s historical clicked news with candidate news to capture their reading interest. However, during this process, even if certain historical clicked news is weakly correlated with the candidate news, the model still interacts with it, generating more noise information when constructing user interests. Simultaneously, the news representation learning process using neural networks is a fine-grained to coarse-grained learning process; i.e., as the network deepens, the fine-grained original news information is easily lost. Therefore, to solve the above problems, this paper proposes a news recommendation model based on hierarchical granular semantic embeddings and threshold attention (HGTA). In HGTA, we propose a threshold attention module to filter unimportant historical clicked news by setting an attention score threshold, i.e., interacting only with historical clicked news that is strongly correlated with the candidate news when capturing user interests. Thus, the model attention is focused, and the noisy interaction information is reduced. Furthermore, when training the neural network, to prevent the loss of fine-grained information, we design a hierarchical granular information extraction module to capture both coarse- and fine-grained news features to better enrich the news semantics. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model achieves highly competitive results in terms of popular metrics. Our code is published at the following URL: https://***/liyiweneven/HGTA.

关键词： Uniform resource locators Training Representation learning Federated learning Semantics Neural networks Noise

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