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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography data mining Mental disorders Multisensory integration

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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Relation extraction with proactive domain adaptation strategy 11

Relation extraction with proactive domain adaptation strateg...

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11th IEEE International Conference on knowledge Graph, ICKG 2020

作者： Zhong, Lingfeng Zhu, Yi Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China Yangzhou University School of Computer Science and Technology Yangzhou China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Relation extraction is an important information extraction task in many Natural Language Processing (NLP) applications, such as automatic knowledge graph construction, question answering, sentiment analysis, etc. However, relation extraction suffers from inappropriate associations between entities when the background knowledge of corpus is insufficiency. Despite the preprocessed external word vector bases can ease this problem, how to find a single word vector base as domain knowledge that contains all the required knowledge features is a huge challenge, and relation extraction with background knowledge is still open to further optimization. To address this problem, in this paper, we propose Relation Extraction method with Proactive Domain Adaptation Strategy (REPDAS for short) to introduce more knowledge features from different knowledge bases. More specifically, firstly, a convolutional network with a parameter-sharing layer is introduced for relation extraction, and word seeds that are important to relational feature exploitation are proactively picked by an attention mechanism during training. Secondly, the proactively-chosen word seeds and the previous parameter-sharing layer are utilized to establish a map between different domains. Our proposed method selectively avails both background knowledge and contextual features for relation extraction by incorporating the convolutional neural network with the proactively domain adaptation strategy. Experiments show that our method effectively enhances the performance of relation extraction compared with other baselines. © 2020 IEEE.

关键词： Domain Adaptation knowledge Graph Relation Extraction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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Recommending courses in MOOCs for jobs: An auto weak supervision approach

arXiv

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arXiv 2020年

作者： Hao, Bowen Zhang, Jing Li, Cuiping Chen, Hong Yin, Hongzhi Key Laboratory of Data Engineering Knowledge Engineering of Ministry of Education School of Information Renmin University of China China School of Information Technology and Electrical Engineering The University of Queensland Australia

The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework (AutoWeakS) via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

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PCQPR: Proactive Conversational Question Planning with Reflection

arXiv

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arXiv 2024年

作者： Guo, Shasha Liao, Lizi Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore

Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions. This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes. In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self- Refining (PCQPR). Concretely, by integrating a planning algorithm inspired by Monte Carlo Tree Search (MCTS) with the analytical capabilities of large language models (LLMs), PCQPR predicts future conversation turns and continuously refines its questioning strategies. This iterative self-refining mechanism ensures the generation of contextually relevant questions strategically devised to reach a specified outcome. Our extensive evaluations demonstrate that PCQPR significantly surpasses existing CQG methods, marking a paradigm shift towards conclusion-oriented conversational question-answering systems. © 2024, CC BY-NC-SA.

关键词： Direct process refining

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Denoi-SpEx+: A Speaker Extraction Network based Speech Dialogue System 17

Denoi-SpEx+: A Speaker Extraction Network based Speech Dialo...

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17th IEEE International Conference on e-Business engineering, ICEBE 2021

作者： Hao, Yun Huang, Xiangkang Huang, Huichou Wu, Qingyao School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Pazhou Lab Guangzhou China University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665444187

The speech dialogue system has gradually been widely used in daily life. Users can consult and communicate with the system through natural language. However, in practical applications, third-person background sounds and background noise interference in real dialogue scenes will be encountered. The uncertainty and complexity of these background sounds will have a bad impact on the recognition of the system. A good speech enhancement module can help us to separate the target speaker from the original speech. Recently, a solution called SpEx+ was proposed from the time domain, but SpEx+ needs a reference speech to assist in training. This reference speech may have noise in actual applications that will affect performance. Therefore, we propose a Denoi-SpEx+ model. Before the reference speech is input to the network, a speech denoising network is added, so that the quality of speech separation in practical applications can be guaranteed. Experiments show that our model can significantly improve the performance of speech separation model of noisy reference speech. © 2021 IEEE.

关键词： Speech enhancement

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Dynamic Graph Pattern Matching in Medical knowledge Graphs 4

Dynamic Graph Pattern Matching in Medical Knowledge Graphs

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4th International Conference on data Intelligence and Security, ICDIS 2022

作者： Du, Xun Tao, Zhenchao Li, Lei School of Computer Science and Information Engineering Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459693

As the study of graph pattern matching (GPM) attracts more and more scholars, the research of GPM in the medical field begins to emerge, from protein structure analysis to breast cancer classification diagnosis, and GPM is more and more widely adopted. However, in medical knowledge graphs (MKG), when the pattern graph dynamically changes, to obtain the matching results of the changed pattern graph, the traditional method has to carry out global matching instead of local matching, which causes a waste of resources and takes a lot of time. To quickly return the matching results of the dynamic pattern graph in MKGs, in this paper, we introduce a caching mechanism to cache the previous matching results and propose a new longest common path matching (M-LCPM) algorithm based on multithreading. The algorithm only performs partial matching on the changed pattern graph and can quickly obtain the matching result. Considering that the M-LCPM algorithm cannot get matching results when the pattern graph changes completely and there may be missing matching subgraphs when the M-LCPM algorithm performs matching, we optimize the M-LCPM algorithm and propose the M-LCPM-Enhance algorithm. Experimental results on two data sets show that our dynamic algorithm is more efficient than existing algorithms. © 2022 IEEE.

关键词： graph pattern matching longest common path medical knowledge graphs

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Detecting Compromised Kernel Hooks with Support of Hardware Debugging Features

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China Communications 2012年第10期9卷 78-90页

作者： Shi Wenchang Zhou HongWei Yuan JinHui Liang Bin School of Information Renmin University of China Beijing 100872 P. R. China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872 P. R. China Information Engineering University of China Zhengzhou 450004 P. R. China

Although there exist a few good schemes to protect the kernel hooks of operating systems, attackers are still able to circumvent existing defense mechanisms with spurious context infonmtion. To address this challenge, this paper proposes a framework, called HooklMA, to detect compromised kernel hooks by using hardware debugging features. The key contribution of the work is that context information is captured from hardware instead of from relatively vulnerable kernel data. Using commodity hardware, a proof-of-concept pro- totype system of HooklMA has been developed. This prototype handles 3 082 dynamic control-flow transfers with related hooks in the kernel space. Experiments show that HooklMA is capable of detecting compomised kernel hooks caused by kernel rootkits. Performance evaluations with UnixBench indicate that runtirre overhead introduced by HooklMA is about 21.5%.

关键词： operating system kernel hook integrity hardware control flow

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A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

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IEEE International Conference on Big knowledge (ICBK)

作者： Wenwu Zhi Yuhong Zhang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs.

关键词： Training Geometry Dictionaries Conferences Bidirectional control Task analysis

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