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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Distinguishability-based genuine nonlocality with genuine multipartite entanglement

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Physical Review A 2023年第2期108卷 022405-022405页

作者： Zong-Xing Xiong Mao-Sheng Li Zhu-Jun Zheng Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou 510006 China School of Mathematics South China University of Technology Guangzhou 510641 China

A set of orthogonal multipartite quantum states is said to be distinguishability-based genuinely nonlocal (also genuinely nonlocal, for abbreviation) if the states are locally indistinguishable across any bipartition of the subsystems. This form of multipartite nonlocality, although more naturally arising than the recently popular “strong nonlocality” in the context of local distinguishability, receives much less attention. In this work, we study the distinguishability-based genuine nonlocality of a typical type of genuine multipartite entangled states—the d-dimensional Greenberger-Horne-Zeilinger (GHZ) states, featuring systems with local dimension not limited to two. In the three-partite case, we find the existence of small genuinely nonlocal sets consisting of these states: we show that the cardinality can at least scale down to linear in the local dimension d, with the linear factor l=1. Specifically, the method we use is semidefinite programming and the GHZ states to construct these sets are special ones which we call “GHZ lattices”. This result might arguably suggest a significant gap between the strength of strong nonlocality and the distinguishability-based genuine nonlocality. Moreover, we put forward the notion of (s,n)-threshold distinguishability and, utilizing a similar method, we successfully construct (2,3)-threshold sets consisting of GHZ states in three-partite systems.

关键词： Nonlocality Quantum correlations in quantum information Quantum entanglement

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Joint QoS Prediction for Web Services based on Deep Fusion of Features

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IEEE Transactions on Network and Service Management 2023年 1-1页

作者： Ding, Linghang Liu, Jianxun Kang, Guosheng Xiao, Yong Cao, Buqing Hunan Provincial Key Lab. for Services Computing and Novel Software Technology and School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE). IEEE

关键词： Feature extraction

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The impact of toxic trolling comments on anti-vaccine YouTube videos (vol 14, 5088, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-9页

作者： Miyazaki, Kunihiro Uchiba, Takayuki Kwak, Haewoon An, Jisun Sasahara, Kazutoshi Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington IN USA Sugakubunka Co. Ltd. Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan

Anti-vaccine trolling on video-hosting websites hinders efforts to increase vaccination rates by using toxic language and threatening claims to intimidate people and promote vaccine hesitancy. However, there is a shortage of research investigating the effects of toxic messages on these platforms. This study focused on YouTube anti-vaccine videos and examined the relationship between toxicity and fear in the comment section of these videos. We discovered that highly liked toxic comments were associated with a significant level of fear in subsequent comments. Moreover, we found complex patterns of contagion between toxicity and fear in the comments. These findings suggest that initial troll comments can evoke negative emotions in viewers, potentially fueling vaccine hesitancy. Our research bears essential implications for managing public health messaging and online communities, particularly in moderating fear-mongering messages about vaccines on social media.

关键词： Emotional contagion Fearful comment Toxic comment Vaccine hesitancy YouTube videos

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Speech Emotion Recognition Using GRU and LSTM

Speech Emotion Recognition Using GRU and LSTM

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IEEE Region 10 Symposium

作者： Vriti Sharma Ritik Raushan Ruchi Verma P.K. Gupta School of Computing and Electrical Engineering IIT Mandi HP India Department of CSE Jaypee University of Information Technology Solan HP India Department of Data Science School of Computing Mohan Babu University Tirupati AP India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Speech emotion recognition (SER) is a hot topic in research, with applications in human-computer interaction, mental health screening, and customer service. In this research, our objective is to develop an accurate and reliable SER system capable of emotion recognition in speech data. We address important issues such as feature extraction, model selection, data imbalance, robustness, and real-time inference using a labeled dataset of various emotional expressions in voice. To extract complicated shapes from auditory characteristics, our method combines deep learning techniques that include long-short-term memory (LSTM) and gated recurrent units (GRU). We carefully manage the data imbalance to ensure that emotions are fairly represented in the training set, and with the implementation of our functional LSTM and GRU models in the training set, we were able to achieve the precision of 62 % and 90 %, respectively.

关键词： Training Emotion recognition Shape Speech recognition Logic gates Feature extraction Robustness Real-time systems Data models Long short term memory

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AI-Driven CASGN Framework for Evaluating Descriptive Answers in Online Education

AI-Driven CASGN Framework for Evaluating Descriptive Answers...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sridevi Ponmalar, P. Vinodini, S. Srithar, V. Morankar, Gouri Jebaseelan, S. D. Sundarsingh Harikrishnan, R. School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India Chennai600119 India PeriyarManiammai Institute of Science & Technology Department of Computer Applications Vallam Thanjavur India Shri Ramdeobaba College of Engineering and Management Nagpur India Sathyabama Institute of Science and Technology Department of Eee Chennai119 India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune412115 India

ISBN: (纸本)9798331543617

There is an enormous demand for automated assessment systems, which can handle the bulk assessments efficiently and effectively. While existing systems perform well in grading objective questions, they break down with subjective or descriptive answers that require a deeper analysis of context and understanding. To address this challenge, we have proposed the AI-Enhanced Context-Aware Semantic Grading Network-state-of-the-art algorithm for instant auto-descriptive response scoring in e-assessments. The AI-CASGN utilizes state-of-the-art AI technologies, particularly NLP and deep learning models-very specifically transformer architectures-during context-based semantic analysis. Beyond mere keyword matching, the system scores what students say and how relevant it is. This takes the concept of grading to an even more fine-tuned art. Scores change with depth and accuracy in the answer given to show complete evaluation. With the dynamic grading framework, adaptation on scores both regarding the accuracy and depth of the answer for a precise evaluation is possible. Inbuilt adaptive learning within the system performance evolves day by day by learning from the new data. AI-CASGN is designed to handle biases in mitigation by considering that there may be different patterns for a response to ensure that responses with varied wording but conceptually correct are fairly graded. It will have to deal with the volumes at scale, hence it is envisaged to process large volumes of responses in minimum time and with high accuracy to be deployed in real time in any educational institution. In future, the system will also provide personalized feedback to students for the purpose of continuous learning and improvement. This represents a scalable, real-time, unbiased tool that assesses subjective answers in modern online learning environments. © 2024 IEEE.

关键词： Semantics

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Coherent Optical Driving of a Superconducting Qubit with an Electro-Optic Transducer

Coherent Optical Driving of a Superconducting Qubit with an ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Warner, Hana K. Holzgrafe, Jeffrey Yankelevich, Beatriz Barton, David Poletto, Stefano Xin, C.J. Sinclair, Neil Zhu, Di Sete, Eyob Langley, Brandon Batson, Emma Colangelo, Marco Shams-Ansari, Amirhassan Joe, Graham Berggren, Karl K. Jiang, Liang Reagor, Matthew Lončar, Marko John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Hyperlight Corporation CambridgeMA02138 United States Rigetti Computing BerkeleyCA94710 United States Research Laboratory for Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60639 United States

We describe coherent optical control of a superconducting qubit with an electro-optic transducer as a step towards enabling optical interconnects between superconducting processor nodes. © Optica Publishing Group... 详细信息

ISBN: (纸本)9781957171395

关键词： Qubits

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Electronic Voting System Using Blockchain technology 3

Electronic Voting System Using Blockchain Technology

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Gupta, Vishan Kumar Jain, Paras Agarwal, Shivani Anupriya Shukla, Vivek Goel, Nilakshi Amity School of Engineering and Technology Amity University Punjab Mohali India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Kothrikalan MP Bhopal India Ajay Kumar Garg Engineering college Department of Information Technology UP Ghaziabad India School of Computing Graphic Era Hill University Uttarakhand Dehradun India Amity Law School Amity University Punjab Mohali India Lloyd Business School Department of Management UP Greater Noida India

ISBN: (纸本)9798331519582

We discover that the voting mechanism has improved and changed with the aid of the blockchain. Due to its effectiveness in preserving voting integrity, blockchain technology in voting systems aids in election process control and fosters voter trust. The following issues plague the traditional voting system: confidence, security, and transparency. The existing system has frequently been described as being extremely susceptible to various threats, including fraud, hacking, and tampering. These issues can be resolved by blockchain technology, which uses an open-source encrypted ledger to keep track of every vote. Every voter in the automated blockchain voting system has a unique digital identity that is verified by blockchain technology. The vote they cast is then recorded on the blockchain system using their identification. Utilizing the blockchain ensures that votes are secure and unchangeable as long as they are registered in the blockchain database. Since the blockchain is an open platform that permits the voting method to be carried out with the assistance of other platforms, this also avoids the issue of a central authority. © 2025 IEEE.

关键词： Voting machines

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Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning

arXiv

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arXiv 2024年

作者： Wang, Jinze Jin, Jiong Zhang, Tiehua Chai, Boon Xian Di Pietro, Adriano Georgakopoulos, Dimitrios Swinburne University of Technology Melbourne Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Department of Computer Science and Technology Tongji University Shanghai China

The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning in automating fault identification, the scarcity of labeled training data, particularly for equipment failure instances, poses a significant challenge. This limitation hampers the development of robust classification models. Existing methods like model-agnostic meta-learning (MAML) do not adequately address variable working conditions, affecting knowledge transfer. To address these challenges, a Related Task Aware Curriculum Meta-learning (RT-ACM) enhanced fault diagnosis framework is proposed in this paper, inspired by human cognitive learning processes. RTACM improves training by considering the relevance of auxiliary sensor working conditions, adhering to the principle of "paying more attention to more relevant knowledge", and focusing on "easier first, harder later" curriculum sampling. This approach aids the meta-learner in achieving a superior convergence state. Extensive experiments on two real-world datasets demonstrate the superiority of RT-ACM framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Curricula

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