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Predictive Theory of Mind Models Based on Public Announcement Logic 1

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5th International Workshop on Dynamic Logic - New Trends and Applications (DaLi)

作者： Top, Jakob Dirk Jonker, Catholijn Verbrugge, Rineke de Weerd, Harmen Univ Groningen Groningen Netherlands Delft Univ Technol Delft Netherlands Leiden Univ Leiden Netherlands

ISBN: (数字)9783031517778

ISBN: (纸本)9783031517761;9783031517778

Epistemic logic can be used to reason about statements such as 'I know that you know that I know that.'. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic statements of arbitrary nesting depth. In contrast, empirical findings on Theory of Mind, the ability to (recursively) reason about mental states of others, show that human recursive reasoning capability has an upper bound. In the present paper we work towards resolving this disparity by proposing some elements of a logic of bounded Theory of Mind, built on Public Announcement Logic. Using this logic, and a statistical method called Random-Effects Bayesian Model Selection, we estimate the distribution of Theory of Mind levels in the participant population of a previous behavioral experiment. Despite not modeling stochastic behavior, we find that approximately three-quarters of participants' decisions can be described using Theory of Mind. In contrast to previous empirical research, our models estimate the majority of participants to be second-order Theory of Mind users.

关键词： Theory of Mind Public Announcement Logic Epistemic Logic Behavioral Modeling Random-Effects Bayesian Model Selection Cognitive science

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MATLAB Implementation of Hp Finite Elements on Rectangles Using Hierarchical Basis Functions 1

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Moskovka, Alexej Valdman, Jan Department of Mathematics Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň30100 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic The Czech Academy of Sciences Institute of Information Theory and Automation Pod vodárenskou věží 4 Prague 818208 Czech Republic

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

A MATLAB implementation of hierarchical shape functions on 2D rectangles is explained and available for download. Global shape functions are ordered for a given polynomial degree according to the indices of the nodes, edges, or elements to which they belong. For a uniform p-refinement, the hierarchical structure enables an effective assembly of mass and stiffness matrices. A solution to a boundary value problem is approximated for various levels of uniform h and p refinements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary value problems

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A Simplified Student Network with Multi-teacher Feature Fusion for Industrial Defect Detection 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Pei, Mingjing Liu, Ningzhong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Electronics and Information Engineering West Anhui University Lu’an China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Improved industrial defect detection is deemed critical for ensuring high-quality manufacturing processes. Despite the effectiveness of knowledge distillation in detecting defects, there are still challenges in extracting useful features and designing a better student network. In this study, a new framework based on knowledge distillation is proposed to address these issues. To avoid overfitting and balance anomaly detection between image and pixel levels, a simplified student decoding network is designed. To extract more diverse features, a multi-teacher network is used in the teacher network. Simple addition and concatenation operations for feature maps were not sufficient, the method fuses features iteratively using attention mechanisms utilized for the multi-teacher networks. The approach is evaluated on two benchmark datasets for industrial defect detection, Mvtec and BTAD, and significantly improved performance is achieved compared to the other methods. An average accuracy of 99.22% and 97.46% for image-level and pixel-level ROC-AUC, respectively, is achieved on the Mvtec dataset, and 93.47% and 96.3% on the BTAD dataset. The proposed framework shows effectiveness in detecting defects in industrial settings, as demonstrated by the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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Human-computer Interaction Approach with Empathic Conversational Agent and computer Vision 10th

Human-Computer Interaction Approach with Empathic Conversati...

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10th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Pereira, Rafael Mendes, Carla Costa, Nuno Frazao, Luis Fernandez-Caballero, Antonio Pereira, Antonio Polytech Leiria Sch Technol & Management Comp Sci & Commun Res Ctr P-2411901 Leiria Portugal Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Inst New Technol Leiria Off INOV INESC Inovacao P-2411901 Leiria Portugal

ISBN: (纸本)9783031611391;9783031611407

The integration of empathy in Human-computer Interaction (HCI) is essential for enhancing user experiences. Current HCI systems often overlook users' emotional states, limiting interaction quality. This research examines the integration of Multimodal Emotion Recognition (MER) into empathic generative-based conversational agents, encompassing facial, body, and speech emotion recognition, along with sentiment analysis. These elements are fused and incorporated into Large Language Models (LLMs) to continuously comprehend and respond to users empathically. This paper highlights the advantages of this multi-modal approach over traditional unimodal systems in recognizing complex human emotions. Additionally, it provides a well-structured background on the addressed topics. The findings include an overview of deep learning in HCI, a review of methods used for emotion recognition and conversational agents, and the proposal of an HCI architecture that integrates facial, body, and speech emotion recognition and sentiment analysis into a fusion model that is fed into an LLM making an empathic conversational agent. This research contributes to the field of HCI by providing an architecture to guide the development of more realistic and meaningful HCIs through MER and a conversational agent.

关键词： Human-computer Interaction Multimodal Emotion Recognition Fusion methods Empathic Conversational Agents

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Multi-step Air Quality Index Forecasting Based on Parallel Multi-input Transformers 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Xie, Jie Li, Jun Zhu, Mingying Wang, Qiong School of Computer and Electronic Information and School of Artificial Intelligence Nanjing Normal University Nanjing210046 China Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China School of Economics Nanjing University 22 Hankou Road Jiangsu Nanjing210093 China Johns Hopkins University Hopkins United States Nanjing Center for Chinese and American Studies Nanjing University 162 Shanghai Road Jiangsu Nanjing China

ISBN: (数字)9783031476655

ISBN: (纸本)9783031476648

Air quality index (AQI) forecasting is a hot research topic that has been widely explored by the whole society. To better understand environmental quality, numerous methods have been proposed for investigating air pollutant data. Previous studies have used deep learning-based methods for AQI forecasting, but few studies investigate parallel multi-input deep models for multi-step hourly AQI forecasting. In this study, a novel parallel multi-input transformer architecture is proposed for multi-step hourly AQI forecasting. To model the air quality data, the transformer is selected and compared with four bidirectional-long-short term memory-based models. Moreover, parallel variable embedding is used to boost forecasting performance. Experimental results using air quality data collected in Shanghai show that our proposed method achieves superior performance against various competing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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CPAKE: Dynamic Batch Authenticated Key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

引用

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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High–Fidelity Haptic Rendering Through Implicit Neural Force Representation 14th

High–Fidelity Haptic Rendering Through Implicit Neural Forc...

引用

14th International Conference on Human Haptic Sensing and Touch Enabled computer Applications, EuroHaptics 2024

作者： Vlachos, Christoforos Moustakas, Konstantinos Department of Electrical and Computer Engineering University of Patras Rion-Patras Greece

ISBN: (纸本)9783031700576

Recent research has demonstrated that neural networks using periodic nonlinearities may be used for implicit representation and reconstruction of continuous-time signals. Starting with a previously published network for representing the Signed Distance Function (SDF) of a mesh surface, we extend the concept and lay the foundation for introducing the additional representation of the Unit Normal Function (UNF). With the representation of these two functions at hand, we construct a penalty-based haptic rendering method. Our experiments suggest that this proposed method is able to handle very large meshes better than other competing alternatives, producing high-fidelity forces, free of discontinuities, by sampling a continuous implicit force function at the desired spatial accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Continuous time systems

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Comparative Analysis of Machine Learning Approaches for Classifying Erythemato-Squamous Skin Diseases 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Kaushik, Bhavana Vijayvargiya, Ankur Uppal, Jayant Gupta, Ankit School of Computer Science University of Petroleum and Energy Studies Dehradun India Accenture Noida India People Strong Noida India Samsung R&D Noida India

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

In the recent era, Machine Learning and Artificial Intelligence have come to a very great development point as we can use ML algorithms to predict the type of Erythemato-Squamous (Skin) diseases of the skin. In Dermatology, differential diagnosis of skin diseases is quite challenging in real life because most skin diseases share many histopathological features. And in this work, Psoriasis, Lichen Planus, Seborrheic Dermatitis, Chronic Dermatitis, Pityriasis Rosea, and Pityriasis Rubra Pilaris are among the skin illnesses for which eight different algorithm analytical comparison is done. Moreover, each classifier algorithm is discussed in detail with its pros and cons. The machine learning algorithms like Support Vector Machine, Decision tree, Random Forest, KNN, Naïve Bayes, Gradient Boosting, XGBoost, and Multilayer Perception have been proven to be successful in preserving state information through exact segmentation/classification. Random forest, Gradient Boosting, and XGBoost outperform all other methods and give an accuracy of 100% on the given ESD dataset. While Support Vector Machine gives the least accuracy of 72.97%. The paper also discusses the difficulties connected with skin disease segmentation or categorization. Furthermore, the study proposes future potential directions that include real-time analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Adaptivity Gap for Influence Maximization with Linear Threshold Model on Trees 1

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17th International Joint Conference on Theoretical computer science - Frontier of Algorithmic Wisdom, IJTCS-FAW 2023

作者： Tao, Yichen Wang, Shuo Yang, Kuan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Zhiyuan College Shanghai Jiao Tong University Shanghai200240 China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9783031393440

ISBN: (纸本)9783031393433

We address the problem of influence maximization within the framework of the linear threshold model, focusing on its comparison to the independent cascade model. Previous research has predominantly concentrated on the independent cascade model, providing various bounds on the adaptivity gap in influence maximization. For the case of a (directed) tree (in-arborescence and out-arborescence), [CP19] and [DPV23] have established constant upper and lower bounds for the independent cascade model. However, the adaptivity gap of this problem on the linear threshold model is not so extensively studied as on the independent cascade model. In this study, we present constant upper bounds for the adaptivity gap of the linear threshold model on trees. Our approach builds upon the original findings within the independent cascade model and employs a reduction technique to deduce an upper bound of 4e2e2-1 for the in-arborescence scenario. For out-arborescence, the equivalence between the two models reveals that the adaptivity gap under the linear threshold model falls within the range of [ee-1,2], as demonstrated in [CP19] under the independent cascade model. © 2023, Springer Nature Switzerland AG.

关键词： Stream flow

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