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Multimodal Dialogue Systems via Capturing Context-aware Dependencies of Semantic Elements 20

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Multimodal Dialogue Systems via Capturing Context-aware Depe...

28th ACM International Conference on Multimedia, MM 2020

作者： He, Weidong Li, Zhi Lu, Dongcai Chen, Enhong Huai, Baoxing Yuan, Jing Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Data Science University of Science and Technology of China China HUAWEI Technologies Hangzhou China

ISBN: (纸本)9781450379885

Recently, multimodal dialogue systems have engaged increasing attention in several domains such as retail, travel, etc. In spite of the promising performance of pioneer works, existing studies usually focus on utterance-level semantic representations with hierarchical structures, which ignore the context-aware dependencies of multimodal semantic elements, i.e., words and images. Moreover, when integrating the visual content, they only consider images of the current turn, leaving out ones of previous turns as well as their ordinal information. To address these issues, we propose a Multimodal diAlogue systems with semanTic Elements, MATE for short. Specifically, we unfold the multimodal inputs and devise a Multimodal Element-level Encoder to obtain the semantic representation at element-level. Besides, we take into consideration all images that might be relevant to the current turn and inject the sequential characteristics of images through position encoding. Finally, we make comprehensive experiments on a public multimodal dialogue dataset in the retail domain, and improve the BLUE-4 score by 9.49, and NIST score by 1.8469 compared with state-of-the-art methods. © 2020 ACM.

关键词： Semantics

Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval

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Towards a Smaller Student: Capacity Dynamic Distillation for...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Xie Huaidong Zhang Xuemiao Xu Jianqing Zhu Shengfeng He South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Huaqiao University Singapore Management University

Previous Knowledge Distillation based efficient image retrieval methods employ a lightweight network as the stu-dent model for fast inference. However, the lightweight stu-dent model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the stu-dent model is gradually compressed during the training. To dynamically adjust the model capacity, our dynamic frame-work inserts a learnable convolutional layer within each residual block in the student model as the channel importance indicator. The indicator is optimized simultaneously by the image retrieval loss and the compression loss, and a retrieval- guided gradient resetting mechanism is proposed to release the gradient conflict. Extensive experiments show that our method has superior inference speed and accu-racy, e.g., on the VeRi-776 dataset, given the ResNet101 as a teacher, our method saves 67.13% model parameters and 65.67% FLOPs without sacrificing accuracy. Code is avail-able at https://***/SCY-X/Capacity_Dynamic_Distillation.

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

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arXiv 2024年

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

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. © 2024, CC BY.

关键词： Bamboo

Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

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arXiv 2023年

作者： Yuan, Haonan Sun, Qingyun Fu, Xingcheng Zhang, Ziwei Ji, Cheng Peng, Hao Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University China Department of Computer Science and Technology Tsinghua University China

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Implementation of an Internal Quality Assurance System at Pablo de Olavide University of Seville: Improving Computer science Students Skills 12th

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Implementation of an Internal Quality Assurance System at Pa...

International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th International Conference on European Transnational Education, ICEUTE 2019

作者： Rubio-Escudero, C. Martínez-Álvarez, F. Atencia-Gil, E. Troncoso, A. Department of Computer Science University of Seville Seville Spain Data Science and Big Data Lab University Pablo de Olavide Seville41013 Spain University Pablo de Olavide Seville41013 Spain

ISBN: (纸本)9783030200046

This work describes how an internal quality assurance system is deployed at Pablo de Olavide University of Seville, Spain, in order to follow up all the existing degrees among the faculties and schools, seven centers inÂ total, and how the teaching-learning process is improved. In the first place, the quality management structure existing in all the centers and degrees of the university is described. Additionally, all the actions related to the quality and improvement of the degrees of a center are reported. Unlike in other Universities, in the Pablo de Olavide University there is no specific procedure for monitoring degrees, but the strategic procedure PE04: Measurement, analysis and improvement of the Internal Quality Assurance System is used to carry out such a procedure. Therefore, the procedure is detailed specifying the different phases it consists of and those responsible for each of them. Once this procedure has been implemented, the centers have a follow-up report for each of their degrees, which also includes an improvement plan to be developed during the next course. The case of the degree of Computer science in Information Systems, included in the School of Engineering, is analyzed over time in order to show how the implementation of such a system improves the overall performance of students. © 2020, Springer Nature Switzerland AG.

关键词： Students

NaFV-Net: An Adversarial Four-view Network for Mammogram Classification 39

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NaFV-Net: An Adversarial Four-view Network for Mammogram Cla...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Feng Hou, Yuxiang Li, Wei Yang, Xiangying Zheng, Haibo Luo, Wenxi Chen, Leqing Cao, Yuyang Liao, Xiaofei Zhang, Yu Yang, Fan Zomaya, Albert 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 Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists’ practices, we propose using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial four-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mammography

Probabilistic modeling of semantic ambiguity for scene graph generation

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arXiv 2021年

作者： Yang, Gengcong Zhang, Jingyi Zhang, Yong Wu, Baoyuan Yang, Yujiu Tsinghua Shenzhen International Graduate School Tsinghua University China University of Electronic Science and Technology of China China Tencent AI Lab China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data China

To generate "accurate" scene graphs, almost all existing methods predict pairwise relationships in a deterministic manner. However, we argue that visual relationships are often semantically ambiguous. Specifically, inspired by linguistic knowledge, we classify the ambiguity into three types: Synonymy Ambiguity, Hyponymy Ambiguity, and Multi-view Ambiguity. The ambiguity naturally leads to the issue of implicit multi-label, motivating the need for diverse predictions. In this work, we propose a novel plug-and-play Probabilistic Uncertainty Modeling (PUM) module. It models each union region as a Gaussian distribution, whose variance measures the uncertainty of the corresponding visual content. Compared to the conventional deterministic methods, such uncertainty modeling brings stochasticity of feature representation, which naturally enables diverse predictions. As a byproduct, PUM also manages to cover more fine-grained relationships and thus alleviates the issue of bias towards frequent relationships. Extensive experiments on the large-scale Visual Genome benchmark show that combining PUM with newly proposed ResCAGCN can achieve state-of-the-art performances, especially under the mean recall metric. Furthermore, we prove the universal effectiveness of PUM by plugging it into some existing models and provide insightful analysis of its ability to generate diverse yet plausible visual relationships. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

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arXiv 2023年

作者： Yin, Mingjia Wang, Hao Xu, Xiang Wu, Likang Zhao, Sirui Guo, Wei Liu, Yong Tang, Ruiming Lian, Defu Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Huawei Singapore Research Center Singapore Huawei Noah's Ark Lab Shenzhen China

The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings. The code is available at https://***/Graph-Team/APGL4SR. © 2023, CC BY.

关键词： Recommender systems

Learning Beams Adaptive to the Environment: An RSRP-based Codebook Design

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Learning Beams Adaptive to the Environment: An RSRP-based Co...

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Xinzhi Ning Shutao Zhang Ye Xue Xi Zheng Qingjiang Shi Tsung-Hui Chang School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

The design of an effective codebook is crucial for the fifth generation (5G) systems, particularly in the context of massive multiple-input-multiple-output (MIMO) and millimeter wave communication. However, existing codebook design methods that rely on the instantaneous channel impulse response (CIR) face challenges in adapting to dynamic channels that undergo small-scale fading changes. To address this issue, this paper proposes a novel two-stage codebook design framework based solely on the reference signal receiving power (RSRP), which provides large-scale statistical information about environment attenuation. The proposed approach involves two stages: first, the angular power spectrum (APS) is estimated through sparse Bayesian learning (SBL), which uses the statistical relationship between APS and RSRP. Second, an iterative clustering and optimization algorithm is used to determine the optimal codebook based on the estimated APS. Experimental results demonstrate that the proposed codebook design framework with a smaller number of beams can adapt to localized environment well and achieves better performance than the classical DFT codebook.

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