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A Review of Human Emotion Synthesis Based on Generative Technology

IEEE Transactions on Affective Computing 2025年

作者： Ma, Fei Xie, Yifan Li, Yukan He, Ying Zhang, Yi Ren, Hongwei Liu, Zhou Yao, Wei Ren, Fuji Yu, Fei Richard Ni, Shiguang Shenzhen China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China MICS Thrust Guangzhou China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen China Carleton University School of Information Technology Canada

Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, and Sequence-to-Sequence Models, have significantly contributed to the development of this field. However, there is a notable lack of comprehensive reviews in this field. To address this problem, this paper aims to address this gap by providing a thorough and systematic overview of recent advancements in human emotion synthesis based on generative models. Specifically, this review will first present the review methodology, the emotion models involved, the mathematical principles of generative models, and the datasets used. Then, the review covers the application of different generative models to emotion synthesis based on a variety of modalities, including facial images, speech, and text. It also examines mainstream evaluation metrics. Additionally, the review presents some major findings and suggests future research directions, providing a comprehensive understanding of the role of generative technology in the nuanced domain of emotion synthesis. © 2025 IEEE.

关键词： Generative adversarial networks

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CRFs for Digital Signature and NIZK Proof System in Web Services 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Kang, Burong Zhang, Lei Yang, Yafang Meng, Xinyu Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Sichuan610041 China Research Institute of China Telecom Corporation Limited Shanghai200062 China School of Computer Science Fudan University Shanghai200433 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Web services are service-oriented computing technology which allows computers running different operating domains to access and share each other’s databases. Each web service is an application (like online business) which may require the private information of users. Thus, it will be important to preserve these web users’ individual privacy. The traditional approaches to achieve this goal in web security is to use the cryptographic technologies, such as digital signature, NIZK proof system. Whereas, some recent research results indicate that these cryptographic technologies may suffer from the algorithm substitution attack (ASA). ASA means that the cryptographic technology would be embedded some backdoor in the process of its implementation by the attacker, and with the backdoor information the attacker can steal the user’s private information. To address this problem, the concept of cryptographic reverse firewall (CRF) has been introduced, which could sanitize the messages inputting and outputting the user’s computer. In this paper, we construct the CRFs for the efficient Pointcheval-Sanders (PS) signature as well as the NIZK proof system. © 2023, Springer Nature Switzerland AG.

关键词： Web services

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Language-driven Grasp Detection with Mask-guided Attention

Language-driven Grasp Detection with Mask-guided Attention

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Tuan Van Vo Minh Nhat Vu Baoru Huang An Vuong Ngan Le Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) GmbH Austria Imperial College London UK Department of Computer Science & Computer Engineering University of Arkansas USA National University of Singapore Singapore Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach.

关键词： Visualization Attention mechanisms Accuracy Service robots Source coding Semantic segmentation Natural languages Grasping Transformers Feature extraction

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Artificial intelligence in cancer target identification and drug discovery

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Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

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Embedding-Based Potential Sales Forecasting of Bread Product

Embedding-Based Potential Sales Forecasting of Bread Product

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作者： Takahashi, Kohei Goto, Yusuke Graduate School of Software and Information Science Iwate Prefectural University 152-52 Sugo Takizawa Iwate020-0693 Japan College of Systems Engineering and Science Shibaura Institute of Technology 307 Fukasaku Minuma-ku Saitama Saitama337-8570 Japan

In this study, we investigate the potential sales forecasts of unhandled bread products in retail stores based on factory shipment data. An embedding-based forecasting method that uses large-scale information network embedding (LINE) and simultaneously considers first- and second-order proximities is developed to define similar neighboring stores using their product-store relationship and to predict their potential sales volume. LINE is a network-embedding method that transforms network data into a lowdimensional distributed representation and requires a low computation time, even when applied to large networks. The results show that our proposed method outperforms a simple prediction method (Baseline) and t-SNE, a well-known dimensionality reduction method for high-dimensional data, in terms of accurate product sales prediction via simulation experiments. Furthermore, we conduct a sensitivity analysis to verify the applicability of our proposed method when the forecasting target is expanded to products sold in fewer stores and in stores with less product variety. © 2022 Fuji Technology Press. All rights reserved.

关键词： Forecasting

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Collaboration And Versioning Framework – A Systematic Top-Down Approach

Collaboration And Versioning Framework – A Systematic Top-D...

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Conference on Model Driven engineering Languages and systems Companion (MODELS-C)

作者： Jakob Pietron Alexander Raschke Joeri Exelmans Matthias Tichy Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Department of Computer Science University of Antwerp – Flanders Make Antwerp Belgium

CONTEXT: Versioning allows users to efficiently create and evolve artifacts, not only but especially in the domain of model-driven engineering. Due to collaboration tools, the place and time where users jointly work at their artifacts do not play an important role anymore. Objective: We systematically elaborated a classification framework for collaboration and versioning tools. The intention is threefold: First, it should be possible to classify existing approaches. Second, deriving from user goals requirements to be met, and capabilities to be supported by a new tool. Third, given a set of capabilities, highlight which user goals can be achieved. Method: According to Kang and Lee, we systematically elaborated a problem space and a solution space and created mappings between both spaces and their sub-spaces. To demonstrate the applicability, we classified existing VCS like git, Google Docs, and several MDE-specific approaches. Results: The created feature diagram covers 238 features and contains about 87 constraints. Conclusion: The developed framework supports researchers and developers in classifying their tools, revealing new opportunities to improve their tools, and guiding the development of new tools.

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AI Trustworthy: Ethical Challenges and Strategies

AI Trustworthy: Ethical Challenges and Strategies

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Jian Liu Iwan Sandjaja Donald C. Wunsch Kummer Institute Center for Artificial Intelligence and Autonomous Systems Missouri University of Science and Technology Rolla USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla USA

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

This paper explores the pivotal role of trust in the widespread application of Artificial Intelligence (AI) across various domains. We review AI applications in sectors like energy, healthcare, and autonomous vehicles and discuss the crisis of human trust they face. This paper introduces a novel framework that delineates the relationship between AI transparency and user trust, highlighting specific industry applications. Through a systematic review of recent literature, we first delve into factors such as emotional response, acceptance, transparency, accuracy, and interpretability that shape human trust in AI. We then underscore the necessity of ethical AI practices and highlight the importance of regulatory measures to keep pace with technological advancements. Finally, we propose strategic measures for maintaining trust in AI, focusing on stringent regulation, ethical authorization, and effective human-AI cooperation, thereby contributing to the ongoing discourse on AI trust.

关键词： Ethics Shape Shape measurement Industry applications Medical services Regulation Artificial intelligence Informatics Systematic literature review Logistics

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Efficient Model Training in Decentralized systems with Federated Learning 2

Efficient Model Training in Decentralized Systems with Feder...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rao, M.S. Srinivasa Saraswathy, S. Neela, V. Arun, M.R. Gupta, Sandeep Taqui, Syed Noeman Vignana Jyothi Institute of Engineering & Technology Vnr Department of Mechanical Engineering Telangana Hyderabad India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India School of Computing and Information Technology Reva University Karnataka Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Department of Electrical Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

In the fast-changing field of machine learning, data privacy and model training efficiency are crucial. Federated Learning (FL) is a breakthrough method for training models on several decentralized devices or servers without centralizing raw data. This study examines Federated Learning's challenges in optimizing model training in dispersed systems. The first stage is to examine the Federated Learning (FL) framework and its merits, particularly in protecting data privacy and reducing server dependence. The next section will discuss communication overhead, stragglers, and non-IID (independent and identically distributed) data issues in a decentralized training model. We developed novel optimization methodologies and adaptive algorithms to improve Federated Learning (FL) efficiency. This includes updating asynchronous models, adaptability-based communication frequency, and non-IID data management. We also present a resilient aggregation method that mitigates Byzantine failures in participating nodes. We demonstrate that our methods improve convergence rate, communication costs, and model performance trained with Federated Learning (FL) by conducting a series of comprehensive experiments and analyzing real-world case studies. We show that these enhancements do not compromise data privacy, preserving Federated Learning (FL) principles. In conclusion, this study advances Federated Learning in decentralized systems. In an age of data sensitivity, this approach could help implement efficient, scalable, and privacy-preserving machine-learning methods. © 2023 IEEE.

关键词： Byzantine Failures Communication Efficiency Data Privacy Decentralized systems Federated Learning Model Training Non-IID Data

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An Improvised Method for Anomaly Detection in social media using Deep Learning

An Improvised Method for Anomaly Detection in social media u...

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2022 International Conference on Electronics and Renewable systems, ICEARS 2022

作者： Sivakumar, C. Sathyanarayanan, D. Karthikeyan, P. Velliangiri, S. Sree Vidyanikethan Engineering College Department of Computer Science and Systems Engineering Andhra Pradesh India Jain University Dept of Cse Bangalore India B v Raju Institute of Technology Dept of Cse Telangana Narasapur India

ISBN: (纸本)9781665484251

Recently, social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. In this paper, a deep learning-based approach is developed to detect the anomalies in social media using text mining. The emotional classification is considered as a part of the model that classifies emotional anomalies present in the text. Classification of such text is conducted via proper training and testing of the classifier. © 2022 IEEE.

关键词： Anomaly detection

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A Robust Image Registration Using Multi-Scale Feature Extraction Techniques Using Deep Learning 4

A Robust Image Registration Using Multi-Scale Feature Extrac...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Nelson Kennedy Babu, C. Sumathi, V. Chokkalingam, Arun Pilvi Systems Inc Solution Architect LewisvilleTX75057 United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College Department of Mathematics Tamil Nadu Chennai44 India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Image registration is a significant assignment in computer visualization and medical imaging, which involves aligning two or more descriptions of the same division or object taken from different viewpoints or at dissimilar epochs. Deep learning-based approaches have recently demonstrated promising outcomes in several image-processing applications, including image registration. Using multi-scale feature extraction methods and deep learning, the study provides a reliable image registration method in this study. Three primary steps make up the suggested method: feature extraction, feature matching, and transformation estimation. A deep convolutional neural network (CNN) is utilized in the feature extraction process to extract multi-scale information from the input images. The CNN architecture is built to manage large-scale deformations by extracting features at various scales. Following feature extraction, a strong matching technique based on the nearest neighbor distance ratio test is utilized to match the extracted features. The proposed method uses the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters robustly, which helps to handle outliers and noise in the feature-matching process. The proposed method is assessed on several benchmark datasets, including the ETH-80 dataset and the TUM RGB-D dataset, and compared with state-of-the-art approaches. The investigational consequences demonstrate that the proposed method outstrips prevailing approaches in terms of accuracy and robustness, especially for images with large-scale deformations. The proposed technique also demonstrates good performance in the presence of noise and outliers, which further demonstrates its robustness. © 2023 IEEE.

关键词： Feature extraction

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