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Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks 24

Quantum Cognition-Inspired EEG-based Recommendation via Grap...

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33rd ACM International conference on Information and Knowledge Management (CIKM)

作者： Han, Jinkun Li, Wei Li, Yingshu Cai, Zhipeng Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9798400704369

current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brain-computer interfaces, it is time to explore next-generation recommenders that show users' real-time thoughts without delay. Electroencephalography (EEG) is a promising method of collecting brain signals because of its convenience and mobility. currently, there is only few research on EEG-based recommendations due to the complexity of learning human brain activity. To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition theory and Graph Convolutional Networks for accurate item recommendations. Compared with the state-of-the-art recommendation models, the superiority of QUARK is confirmed via extensive experiments.

关键词： Recommendation System Quantum Cognition theory Graph Neural Network EEG Data Personalization

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33rd European Symposium on Programming, ESOP 2024, held as part of the European Joint conferences on theory and practice of Software, ETAPS 2024

33rd European Symposium on Programming, ESOP 2024, held as p...

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33rd European Symposium on Programming, ESOP 2024, held as part of the European Joint conferences on theory and practice of Software, ETAPS 2024

ISBN: (纸本)9783031572661

The proceedings contain 30 papers. The special focus in this conference is on Programming. The topics include: Monadic Intersection Types, Relationally;layered Modal Type theory: Where Meta-programming Meets Intensional Analysis;program Synthesis from Graded Types;a Formal Treatment of Bidirectional Typing;generic bidirectional typing for dependent type theories;artifact report: Generic bidirectional typing for dependent type theories;deciding Subtyping for Asynchronous Multiparty Sessions;the Session Abstract Machine;trocq: Proof Transfer for Free, With or Without Univalence;artifact Report: Trocq: Proof Transfer for Free, With or Without Univalence;Observational Equality Meets CIC;definitional Functoriality for Dependent (Sub)Types;artifact Description.

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33rd European Symposium on Programming, ESOP 2024, held as part of the European Joint conferences on theory and practice of Software, ETAPS 2024

33rd European Symposium on Programming, ESOP 2024, held as p...

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33rd European Symposium on Programming, ESOP 2024, held as part of the European Joint conferences on theory and practice of Software, ETAPS 2024

ISBN: (纸本)9783031572616

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PAGE: Equilibrate Personalization and Generalization in Federated Learning 24

PAGE: Equilibrate Personalization and Generalization in Fede...

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33rd ACM Web conference, WWW 2024

作者： Chen, Qian Wang, Zilong Hu, Jiaqi Yan, Haonan Zhou, Jianying Lin, Xiaodong Xidian University Xi'an China Singapore University of Technology and Design Singapore Singapore University of Guelph Guelph Canada

ISBN: (纸本)9798400701719

Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server). Representing clients' current demands and the server's future demand, local model personalization and global model generalization are separately investigated, as the ill-effects of data heterogeneity enforce the community to focus on one over the other. However, these two seemingly competing goals are of equal importance rather than black and white issues, and should be achieved simultaneously. In this paper, we propose the first algorithm to balance personalization and generalization on top of game theory, dubbed PAGE, which reshapes FL as a co-opetition game between clients and the server. To explore the equilibrium, PAGE further formulates the game as Markov decision processes, and leverages the reinforcement learning algorithm, which simplifies the solving complexity. Extensive experiments on four widespread datasets show that PAGE outperforms state-of-the-art FL baselines in terms of global and local prediction accuracy simultaneously, and the accuracy can be improved by up to 35.20% and 39.91%, respectively. In addition, biased variants of PAGE imply promising adaptiveness to demand shifts in practice. © 2024 ACM.

关键词： Game theory

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Insights from Univalent Foundations: A Case Study Using Double Categories 33

Insights from Univalent Foundations: A Case Study Using Doub...

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33rd EACSL Annual conference on computer science Logic, CSL 2025

作者： Rasekh, Nima van der Weide, Niels Ahrens, Benedikt North, Paige Randall Institute of Mathematics and Computer Science University of Greifswald Germany Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Delft University of Technology Netherlands University of Birmingham United Kingdom Department of Information and Computing Sciences Mathematical Institute Utrecht University Netherlands

ISBN: (纸本)9783959773621

Category theory unifies mathematical concepts, aiding comparisons across structures by incorporating not just objects, but also morphisms capturing interactions between objects. Of particular importance in some applications are double categories, which are categories with two classes of morphisms, axiomatizing two different kinds of interactions between objects. These have found applications in many areas of mathematics and theoretical computer science, for instance, the study of lenses, open systems, and rewriting. However, double categories come with a wide variety of equivalences, which makes it challenging to transport structure along equivalences. To deal with this challenge, we propose the univalence maxim: each notion of equivalence of categorical structures has a corresponding notion of univalent categorical structure which induces that notion of equivalence. We also prove corresponding univalence principles, which allow us to transport structure and properties along equivalences. In this way, the usually informal practice of reasoning modulo equivalence becomes grounded in an entirely formal logical principle. We apply this perspective to various double categorical structures, such as (pseudo) double categories and double bicategories. Concretely, we characterize and formalize their definitions in Coq UniMath up to chosen equivalences, which we achieve by establishing their univalence principles. © Nima Rasekh, Niels van der Weide, Benedikt Ahrens, and Paige Randall North.

关键词： Choquet integral

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Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets 24

Toward Practical Entity Alignment Method Design: Insights fr...

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33rd ACM Web conference, WWW 2024

作者： Jiang, Xuhui Xu, Chengjin Shen, Yinghan Wang, Yuanzhuo Su, Fenglong Shi, Zhichao Sun, Fei Li, Zixuan Guo, Jian Shen, Huawei Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Science Beijing China IDEA Research International Digital Economy Academy Guangdong Shenzhen China Institute of Computing Technology Chinese Academy of Sciences Beijing China National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9798400701719

The flourishing of knowledge graph (KG) applications has driven the need for entity alignment (EA) across KGs. However, the heterogeneity of practical KGs, characterized by differing scales, structures, and limited overlapping entities, greatly surpasses that of existing EA datasets. This discrepancy highlights an oversimplified heterogeneity in current EA datasets, which obstructs the exploration of the EA application. In this paper, we study the performance of EA methods on the alignment of highly heterogeneous KGs (HHKGs). Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios. Then, based on these datasets, we conduct extensive experiments to evaluate previous representative EA methods. Our findings reveal that, in aligning HHKGs, valuable structure information can hardly be exploited, which leads to inferior performance of existing EA methods, especially those based on GNNs. These findings shed light on the potential problems associated with the conventional application of GNN-based methods as a panacea for all EA datasets. Consequently, to elucidate what EA methodology is genuinely beneficial in practical scenarios, we undertake an in-depth analysis by implementing a simple but effective approach: Simple-HHEA. Our experiment results conclude that the key to the future EA model design in practice lies in their adaptability and efficiency to varying information quality conditions, as well as their capability to capture patterns across HHKGs. The datasets and source code are available at https://***/IDEA-FinAI/Simple-HHEA. © 2024 Owner/Author.

关键词： Alignment

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Innovative Application of Genetic Algorithms in the computer Games 33

Innovative Application of Genetic Algorithms in the Computer...

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第33届中国控制与决策会议

作者： Jun Tao Gui Wu Zhentong Yi Peng Zeng School of Artificial Intelligence Jianghan University Department of Electrical&Computer Engineering Rowan University Educational Administration office Jianghan University Graduate School Jianghan University

ISBN: (纸本)9781665431293

The traditional methods to solve the computer games problem are to use various search algorithms on the game search tree and to combine the situation evaluation to generate the corresponding *** paper introduces the innovative genetic algorithms to the computer games based on the traditional *** the operations for the search tree such as selection,crossover and mutation,the better search tree can be gotten from the better *** the same time,the application of the evaluation functions can produce the best steps in the current *** advanced and modified genetic algorithms are proved to be practical and applicative by experimentations and tests of computer games.

关键词： computer Games Genetic Algorithm Search Tree Search Algorithms

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Research on Thread Optimization and Opening Library Based on Parallel PVS Algorithm in Amazon Chess 33

Research on Thread Optimization and Opening Library Based on...

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第33届中国控制与决策会议

作者： Jingyuan Ju Hongkun Qiu FuShuai Wang XueJie Wang YaJie Wang School of Artificial Intelligence Academy Shenyang Aerospace University School of Computer Science Shenyang Aerospace University Engineering Training Center Shenyang Aerospace University College of Automation Shenyang Aerospace University

ISBN: (纸本)9781665431293

This paper introduces the main algorithms used in Amazon game system,and mainly studies the number of threads in parallel PVS algorithm based on *** view of the low utilization of computer hardware by the current algorithm,this paper improves the efficiency of the algorithm by changing the number of parallel algorithm threads to be equal to the number of CPU cores,and draws a conclusion that the multi-core resources can be maximized when the number of parallel algorithm threads is equal to the number of CPU *** paper studies the design of the opening Library of Amazon chess,puts forward the design idea of the opening Library of Amazon chess and the necessity of the opening library system.

关键词： computer Game Amazon Game Search algorithm Parallel efficiency Opening library

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Design and Development of Teaching System for Manipulator Simulation Control 33

Design and Development of Teaching System for Manipulator Si...

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第33届中国控制与决策会议

作者： Yunduo Jiang Yihe Yang Chunfeng Shao Shuying Zhao College of Information Science and Engineering Northeastern University College of Mechanical Engineering Northeastern University

ISBN: (纸本)9781665431293

The Manipulator is widely used in the current industrial field,and its teaching has a strong theoretical and practical ***,traditional theoretical teaching is often out of touch with practice,resulting in poor teaching *** paper develops a teaching software system for Manipulator simulation control,provides an interactive and visual learning platform for Manipulator principles,and deepens its understanding of teaching content through PBL teaching,reduces the difficulty of course learning,and improves students Interest in learning.

关键词： Manipulator simulation Control Teaching system design PBL December

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G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification 33

G2NetPL: Generic Game-Theoretic Network for Partial-Label Im...

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33rd British Machine Vision conference Proceedings, BMVC 2022

作者： Abdelfattah, Rabab Zhang, Xin Fouda, Mostafa M. Wang, Xiaofeng Wang, Song Department of Electrical Engineering University of South Carolina South Carolina United States Department of Electrical and Computer Engineering Idaho State University Idaho United States Department of Computer Science University of South Carolina South Carolina United States

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every training image. Existing works on partial-label learning focus on the case where each training image is labeled with only a subset of its positive/negative labels. To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled. In G2NetPL, each unobserved label is associated with a soft pseudo label, which, together with the network, formulates a two-player non-zero-sum non-cooperative game. The objective of the network is to minimize the loss function with given pseudo labels, while the pseudo labels will seek convergence to 1 (positive) or 0 (negative) with a penalty of deviating from the predicted labels determined by the network. In addition, we introduce a confidence-aware scheduler into the loss of the network to adaptively perform easy-to-hard learning for different labels. Extensive experiments demonstrate that our proposed G2NetPL outperforms many state-of-the-art multi-label classification methods under various partial-label settings on three different datasets. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Game theory

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