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2022 2nd International Conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2022

作者： Wang, Lei Wang, Xili School of Computer Science Shaanxi Normal University Xi’an China School of Mathematics and Computer Application Shangluo University Shangluo China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China

ISBN: (纸本)9783800760268

Graph neural network (GNN) has shown remarkable performance in many fields, such as edge prediction, node classification, etc. However, the traditional graph neural network can only accept the full graph to train the model, which leads to a very high computational complexity. Besides, the lack of multi-scale mechanism limits the actual classification performance. In this paper, we propose a multiscale fusion model based on graph convolutional network (GCN) and convolutional neural network (CNN) for hyperspectral image (HSI) classification tasks. This model can first sample randomly from the full graph to subgraphs of different scales, then trains GCN on sub-graphs of different scales and extract features, and finally integrates the spatial-spectral features extracted by multi-scale CNN classifier and the structural features extracted by multi-scale GCN for end-to-end mini-batch joint training. Experiments on two real HSI data s demonstrate that the proposed method significantly improves the classification performance compared with other comparison methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Graph neural networks

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CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness 39

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Song, Shoucheng Lin, Youfang Han, Sheng Yao, Chang Wu, Hao Wang, Shuo Lv, Kai School of Computer Science & Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China

ISBN: (纸本)157735897X

Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challenging to meet in practice. However, real-world agents suffer from channel delays, receiving messages sent at different time points, termed Asynchronous Communication, leading to cognitive biases and breakdowns in collaboration. This paper first defines two communication delay settings in MARL and emphasizes their harm to collaboration. To handle the above delays, this paper proposes a novel framework Communication Delay-tolerant Multi-Agent Collaboration (CoDe). At first, CoDe learns an intent representation as messages through future action inference, reflecting the stable future behavioral trends of the agents. Then, CoDe devises a dual alignment mechanism of intent and timeliness to strengthen the fusion process of asynchronous messages. In this way, agents can extract the long-term intent of others, even from delayed messages, and selectively utilize the most recent messages that are relevant to their intent. Experimental results demonstrate that CoDe outperforms baseline algorithms in three MARL benchmarks without delay and exhibits robustness under fixed and time-varying delays. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Delay tolerant networks

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Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval 39

Statistical Model-driven Similarity Hashing: Bridging Modali...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kuai, Mingjin Long, Jun Yang, Zhan School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Changsha410083 China

ISBN: (纸本)157735897X

Unsupervised deep cross-modal hash retrieval aims to map multi-modal features into binary hash codes without labels, which is of interest due to its storage efficiency, query speed and convenient applications. However, existing approaches suffer from two main limitations: (1) Slightly insufficient consideration of text instance similarity, along with independent or redundant fusion to learn multi-modal similarity information. (2) They ignore the noisy adjacent correlations between multi-modal instances, leading to a lack of discriminative power in the generated hash codes. To address these challenges, we propose a new approach called Statistical Model-driven Similarity Hashing (SMSH). Specifically, we introduce Jaccard similarity when constructing the text similarity matrix. It reduces the similarity error between text instances while better considering the asymmetry of the elements in the text features. After that, we integrate the original similarity information between various modalities to construct a unified similarity matrix. The gaps between modalities are bridged while reducing the redundant information in them. In addition, we introduce a Statistical Model-driven Similarity Enhancement (SMSE) approach, which reduces the noise of similarity relations between multi-modal instances by using a Gaussian Mixture Model to keep instances with lower semantic similarity as far away from each other as possible. Experiments on three benchmark datasets demonstrate the excellent performance of the SMSH method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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The Evaluation of Trust Towards The Intention to Use of Blockchain-Based Crowdwork Systems (Case Study of Creative Industry) 8

The Evaluation of Trust Towards The Intention to Use of Bloc...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Hartono, Sugiarto Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350326093

Current crowdwork research still has issues related to trust. Workers and employers do not know each other, so problems related to trust will arise. This trust issue will influence the desire to use the crowdwork system. This study will evaluate trust in the intention to use blockchain-based crowdwork systems in creative industries. The methodology used is a quantitative approach (questionnaire) which is distributed to 200 respondents consisting of workers and employers. After that, testing the inner and outer models was carried out. The results obtained are technology, risk, and reputation which have the greatest impact on trust. The use of blockchain technology can minimize risks when using a crowdwork system, such as data that cannot be changed, data tracing can be done, and so on. The rating and review features are also important to be able to find out the user's reputation in the system. © 2023 IEEE.

关键词： Blockchain

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning

arXiv

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arXiv 2023年

作者： Liu, Jiayu Huang, Zhenya Zhai, Chengxiang Liu, Qi Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China University of Illinois at Urbana-Champaign United States

Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge "learning" and "applying" organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Invisible Watermark and Hypothesis Testing for Multi-Ownership Verification Framework of Deep Neural Network

Invisible Watermark and Hypothesis Testing for Multi-Ownersh...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ruoxi Wang Yujia Zhu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid development of information technology, artificial intelligence models have become the core asset of tech giants, highlighting the importance of copyright protection. Black-box watermarking has gained attention due to its ability to verify the legality of models remotely without relying on internal model information. The challenges lie in embedding and verifying watermarks without accessing internal model parameters, while ensuring the robustness and stealth of the watermark. This paper focuses on discussing effective ways to protect the copyright of black-box watermarking models and detect and prevent unauthorized user behavior that infringes on model ownership. Therefore, this paper proposes a multi-ownership verification framework based on invisible watermark and hypothesis testing, which mainly consists of two parts: watermark generation and copyright verification. The watermark generation uses a dynamic watermarking method to generate invisible backdoor watermark and verifies copyright through hypothesis testing. This framework has high assurance and effectiveness under various models of CIFAR-10 and MNIST datasets and shows strong robustness in VGG and Resnet model pruning attacks. This research provides new ideas and methods for the protection of model copyrights in deep neural networks.

关键词： Deep learning Education Closed box Watermarking Artificial neural networks Copyright protection Benchmark testing Robustness Protection Information technology

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Mobile Application for Elderly Care 2

Mobile Application for Elderly Care

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Jose, Fernando Livaldo Mahardhika, Mozan Hafiz Sukiman, Nathanael Adolf Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Utomo, Wiranto Herry Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara Univerisity Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia President University Faculty of Computing Department of Magister Science of Information Technology West Java17530 Indonesia

ISBN: (纸本)9781665400527

This paper discusses the design and implementation process of mobile applications used by nurses to communicate with the elderly or with people appointed to represent the elderly in using this mobile application. This mobile application is expected to help caregivers or nurses monitor the health condition of the elderly, and this mobile application has several functions that make it complete. These functions include submitting weekly reports, communicating with others via forums, and providing relevant information about the elderly and how to deal with them, including news and government regulations on elderly care. This mobile application uses case diagrams to describe business processes and class diagrams to illustrate database design. As for the implementation, android studio and MySQL database are used for database storage. This paper is limited to 9 references for literature review previous similar research by searching for publication titles with the sentences 'mobile application'+elderly+care using quotation marks and without quotation marks for the words elderly and care for all years of publication. © 2022 IEEE.

关键词： Mobile computing

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Linking Multi-Scale Brain Connectivity with Vigilance, Working Memory, and Behavior in Adolescents 22

Linking Multi-Scale Brain Connectivity with Vigilance, Worki...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Bajracharya, Prerana Ballem, Ram Chen, Jiayu Andrés-Camazón, Pablo Khasayeva, Nigar Calhoun, Vince Liu, Jingyu Iraji, Armin Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States Georgia State University Department of Computer Science Atlanta United States Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM Cibersam Isciii School of Medicine Universidad Complutense Madrid Spain

ISBN: (纸本)9798331520526

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in 'the fronto-thalamo-cerebellar (FTC) circuitry' and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD). © 2025 IEEE.

关键词： Behavioral research

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