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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Modeling complex logistics systems using soft computing methodology of fuzzy cognitive maps

Modeling complex logistics systems using soft computing meth...

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2011 7th IEEE International Conference on Automation Science and Engineering, CASE 2011

作者： Stylios, Chrysostomos D. Georgoulas, George Knowledge and Intelligent Computing Laboratory Dept. of Informatics and Telecommunications Technology Technological Educational Institute of Epirus 47100 Artas Greece

ISBN: (纸本)9781457717307

Fuzzy Cognitive Maps (FCMs) is an abstract soft computing modeling methodology that has been applied in many areas quite successfully. In this paper we discuss its modeling applicability to complex logistics systems involved in an intermodal container terminal and the way it could represent and handle the vast amount of information by an abstract point of view based on a decentralized approach, where the supervisor of the system is modeled as an FCM. We also investigate its applicability as a metamodel of the intermodal terminal in a simulation-optimization framework. Experts have a key role in developing the FCM as they describe a general operational and behavioral model of the system using concepts for the main aspects of the system, and weighted directed edges to represent causality. On the other hand, when data is available, data driven approaches have also been proposed for the development of FCM models. The FCM representation and implementation is discussed to develop a behavioral model of any complex system mainly based on a hierarchical structure, as well as its use as a metamodel of the system. © 2011 IEEE.

关键词： Fuzzy Cognitive Maps

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An automatic semantic relationships discovery approach 04

An automatic semantic relationships discovery approach

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13th International World Wide Web Conference on Alternate Track, Papers and Posters, WWW Alt. 2004

作者： Zhuge, Hai Zheng, Liping Zhang, Nan Li, Xiang China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)1581139128

An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for constructing the semantic link network. The basic premise of this work is that the semantics of a web page can be reflected by a set of keywords, and the semantic relationship between two web pages can be determined by the semantic relationship between their keyword sets. The approach adopts the data mining algorithms to discover the semantic relationships between keyword sets, and then uses deductive and analogical reasoning to enrich the semantic relationships. The proposed algorithms have been implemented. Experiment shows that the approach is feasible.

关键词： Data mining

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The superalignment of superhuman intelligence with large language models

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SCIENCE CHINA Information Sciences 2025年第6期68卷

作者： Minlie HUANG Yingkang WANG Shiyao CUI Pei KE Jie TANG The CoAI Group Department of Computer Science and Technology Tsinghua University Beijing 100084 China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu 611731 China Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing 100084 China

We have witnessed the emergence of superhuman intelligence thanks to the fast development of large language models (LLMs) and multimodal language models. As the application of such superhuman models becomes increasingly popular, a critical question arises: how can we ensure they still remain safe, reliable, and aligned well with human values encompassing moral values, Schwartz's Values, ethics, and many more?In this position paper, we discuss the concept of superalignment from a learning perspective to answer this question by outlining the learning paradigm shift from large-scale pretraining and supervised fine-tuning, to alignment training. We define superalignment as designing effective and efficient alignment algorithms to learn from noisy-labeleddata (point-wise samples or pair-wise preference data) in a scalableway when the task is very complex for human experts to annotate and when the model is stronger than human *** highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation. We then present a conceptual framework for superalignment, which comprises three modules: an attackerwhich generates the adversary queries trying to expose the weaknesses of a learner model, a learnerwhich refines itself by learning from scalable feedbacks generated by a critic model with minimal human experts, and a criticwhich generates critics or explanations for a given query-response pair, with a target of improving the learner by *** discuss some important research problems in each component of this framework and highlight some interesting research ideas that are closely related to our proposed framework, for instance, self-alignment, self-play, self-refinement, and more. Last, we highlight some future research directions for superalignment, including the identification of new emergent risks and multi-dimensional alignment.

关键词： superalignment superhuman intelligence large language models scalable feedback weak-to-strong generalization

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Modeling the growth of future web1 13

Modeling the growth of future web1

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13th International World Wide Web Conference on Alternate Track, Papers and Posters, WWW Alt. 2004

作者： Zhuge, Hai Chen, Xue Li, Xiang China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)1581139128

The future Web can be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth -?adding it to the network -?to death -?removing it from the network. Through establishing and investigating two types of models for such a network, we obtain the same scale free distribution of semantic links. Simulations and comparisons validate the rationality of the proposed models.

关键词： Semantic Web

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Distributed solving Sylvester equations with fractional order dynamics

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Control Theory and Technology 2021年第2期19卷 249-259页

作者： Songsong Cheng Shu Liang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and AutomationAnhui UniversityHefei230601AnhuiChina Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China

This paper addresses distributed computation Sylvester equations of the form AX+XB=C with fractional order *** partitioning parameter matrices A,B and C,we transfer the problem of distributed solving Sylvester equations as two distributed optimization models and design two fractional order continuous-time algorithms,which have more design freedom and have potential to obtain better convergence performance than that of existing first order ***,rewriting distributed algorithms as corresponding frequency distributed models,we design Lyapunov functions and prove that proposed algorithms asymptotically converge to an exact or least squares ***,we validate the effectiveness of proposed algorithms by providing a numerical example.

关键词： Fractional order calculus Distributed optimization Sylvester equation Frequency distributed model Convergence

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Tourette Syndrome's Coprolalia Explanation using Bipartite Graph Matching of Thoughts and Game Theoretic Model for Symptoms Minimisation 7

Tourette Syndrome's Coprolalia Explanation using Bipartite G...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Spyrou, Evangelos D. Nestoris, Christoforos Stylios, Chrysostomos University of Ioannina Laboratory of Knowledge and Intelligent Computing Dept. of Informatics and Telecommunications Arta Greece Psychiatrist Pavlou Mela 26 Thessaloniki54622 Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Coprolalia is a symptom of Tourette's syndrome, which is essentially involuntary cursing. The obscene words usually have a meaning, which is associated with offending social acquaintances often based on locality or sexuality. This results in a patient's social isolation and increased anxiety. The patient finds herself stopping things that she would do in her everyday life. In this work, we aim to explain the Coprolalia symptom with the means of bipartite graph matching. We assume that the brain network performs some kind of rerouting of thoughts, which results in the association of normal thoughts with their matched obscene ones. With this we aim to decode the operation that results in Coprolalia, in order for the patient to be informed of this behaviour. Furthermore, we propose a game-theoretic model based on the Battle of the Sexes, which has two equilibria in the pure strategies, namely to jam the intrusive coprolalic thoughts or to pass through the brain. In this way, we provide a mechanism for the patient to stand in social activities, in which the symptoms are severe. Moreover, we align with Cognitive Behavioural Therapy (CBT), with which intrusive thoughts pass through the brain with the aim of deteriorating. © 2022 IEEE.

关键词： Game theory

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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning

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IFAC-PapersOnLine 2017年第1期50卷 4727-4732页

作者： Mansouri S.S. Karvelis P. Georgoulas G. Nikolakopoulos G. Robotics Group Control Engineering Division of the Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden Laboratory of Knowledge and Intelligent Computing Department of Computer Engineering Technological Educational Institute of Epirus Arta Greece

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions. © 2017

关键词： Battery Machine Learning Prediction Remaining Useful Life UAVs

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Article reranking by memory-enhanced key sentence matching for detecting previously fact-checked claims 59

Article reranking by memory-enhanced key sentence matching f...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

ISBN: (纸本)9781954085527

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021 Association for Computational Linguistics

关键词： Semantics

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Medical Decision Support Systems based on Soft computing techniques

Medical Decision Support Systems based on Soft Computing tec...

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作者： Stylios, Chrysostomos D. Georgopoulos, Voula C. Knowledge and Intelligent Computing Laboratory Department of Informatics and Telecommunications Technology Technological Educational Institute of Epirus 47100 Artas Greece Department of Speech and Language Therapy Technological Educational Institute of Patras Koukouli 26334 Patras Greece

ISBN: (纸本)9783902661937

Medical Decision Support Systems (MDSS) are very important constructions that are incorporated into intelligent Information E-Health Systems aiming to produce warnings or to consult and suggest clinical judgments either to inexperienced medical professionals or in their lighter versions to the general public through medical advisors websites. Soft computing (SC) techniques, especially those that are based on exploiting human knowledge and experience, are extremely useful to model complex decision making procedures and thus, they have a key role in development such MDSS. Such a modeling methodology is Fuzzy Cognitive Maps which is suitable to represent human reasoning and to infer conclusions and decisions in a human-like way. In order to develop an integrated stand alone MDSS, Fuzzy Cognitive Maps could be complemented by other Soft computing techniques such as Genetic Algorithms and/or Case Based Reasoning and so to construct more efficient advanced Medical Decision Support Systems. The synergism and complementary of these methodologies may pave the way to new sophisticated systems. © 2011 IFAC.

关键词： Fuzzy Cognitive Maps

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