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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Xu, Xiaofei Sheng, Quan Z. Benatallah, Boualem Chen, Zhong Gazda, Robert Saddik, Abdulmotableb Ei Singh, Munindar P. Faculty of Computing Harbin Institute of Technology China Macquarie University School of Computing NSW2109 Australia Dublin City University School of Computing Ireland Peking University School of Computer Science China Wireless Networking Lab Interdigital United States University of Ottawa School of Electrical Engineering and Computer Science Canada North Carolina State University Department of Computer Science United States

ISBN: (纸本)9798350304855

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed. © 2023 IEEE.

关键词： Internet of things

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Automatic Curriculum for Unsupervised Reinforcement Learning 24

Automatic Curriculum for Unsupervised Reinforcement Learning

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Yucheng Yang Tianyi Zhou Lei Han Meng Fang Mykola Pechenizkiy Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics and Computer Science University of Maryland College Park College Park MD USA Tencent AI Lab Shenzhen China Department of Mathematics and Computer Science University of Liverpool & Eindhoven University of Technology Liverpool United Kingdom

ISBN: (纸本)9798400704864

Unsupervised reinforcement learning (URL) relies on carefully designed training objectives rather than task rewards to learn general skills. However, we lack quantitative evaluation metrics for URL but mainly rely on visualizations of trajectories for comparison. Moreover, most URL methods choose to optimize a single training objective, which may hinder later-stage learning and the development of new skills. To bridge these gaps, we first introduce a combination of metrics that can evaluate diverse properties of URL. We show that balancing these metrics in URL leads to better performance and trajectories with empirical evidence and theoretical insights. Next, we develop an automatic curriculum that uses a non-stationary multi-armed bandit algorithm to select URL objectives for different learning episodes, resulting in a balanced improvement on all the metrics. Extensive experiments in different environments demonstrate the advantages of our method in achieving promising and balanced performance on multiple metrics when compared to recent URL methods.

关键词： auto curriculum learning intrinsic motivation unsupervised reinforcement learning

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The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR

The Second Multi-Channel Multi-Party Meeting Transcription C...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Liang, Yuhao Shi, Mohan Yu, Fan Li, Yangze Zhang, Shiliang Du, Zhihao Chen, Qian Xie, Lei Qian, Yanmin Wu, Jian Chen, Zhuo Lee, Kong Aik Yan, Zhijie Bu, Hui School of Computer Science China Alibaba Group Speech Lab of Damo Academy China NERC-SLIP China Shanghai Jiao Tong University SpeechLab Department of Computer Science and Engineering China Singapore Institute of Technology Ict Cluster Singapore Institute for Infocomm Research A∗star Singapore Beijing Shell Shell Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350306897

With the success of the first Multi-channel Multi-party Meeting Transcription challenge (M2MeT), the second M2MeT challenge (M2MeT 2.0) held in ASRU2023 particularly aims to tackle the complex task of speaker-attributed ASR (SAASR), which directly addresses the practical and challenging problem of 'who spoke what at when' at typical meeting scenario. We particularly established two sub-tracks. The fixed training condition sub-track, where the training data is constrained to predetermined datasets, but participants can use any open-source pre-trained model. The open training condition sub-track, which allows for the use of all available data and models without limitation. In addition, we release a new 10-hour test set for challenge ranking. This paper provides an overview of the dataset, track settings, results, and analysis of submitted systems, as a benchmark to show the current state of speaker-attributed ASR. © 2023 IEEE.

关键词： Alimeeting M2MeT 2.0 Meeting Transcription Multi-speaker ASR Speaker-attributed ASR

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TASK-CUSTOMIZED MASKED AUTOENCODER VIA MIXTURE OF CLUSTER-CONDITIONAL EXPERTS

arXiv

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arXiv 2024年

作者： Liu, Zhili Chen, Kai Han, Jianhua Hong, Lanqing Xu, Hang Li, Zhenguo Kwok, James T. Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Huawei Noah's Ark Lab Canada

Masked Autoencoder (MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training. However, when the various downstream tasks have data distributions different from the pre-training data, the semantically irrelevant pre-training information might result in negative transfer, impeding MAE's scalability. To address this issue, we propose a novel MAE-based pre-training paradigm, Mixture of Cluster-conditional Experts (MoCE), which can be trained once but provides customized pre-training models for diverse downstream tasks. Different from the mixture of experts (MoE), our MoCE trains each expert only with semantically relevant images by using cluster-conditional gates. Thus, each downstream task can be allocated to its customized model pre-trained with data most similar to the downstream data. Experiments on a collection of 11 downstream tasks show that MoCE outperforms the vanilla MAE by 2.45% on average. It also obtains new state-of-the-art self-supervised learning results on detection and segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection

arXiv

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arXiv 2024年

作者： Cao, Yang Zeng, Yihan Xu, Hang Xu, Dan The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Huawei Noah’s Ark Lab Canada

Open-vocabulary 3D Object Detection (OV-3DDet) addresses the detection of objects from an arbitrary list of novel categories in 3D scenes, which remains a very challenging problem. In this work, we propose CoDAv2, a unified framework designed to innovatively tackle both the localization and classification of novel 3D objects, under the condition of limited base categories. For localization, the proposed 3D Novel Object Discovery (3D-NOD) strategy utilizes 3D geometries and 2D open-vocabulary semantic priors to discover pseudo labels for novel objects during training. 3D-NOD is further extended with an Enrichment strategy that significantly enriches the novel object distribution in the training scenes, and then enhances the model’s ability to localize more novel objects. The 3D-NOD with Enrichment is termed 3D-NODE. For classification, the Discovery-driven Cross-modal Alignment (DCMA) module aligns features from 3D point clouds and 2D/textual modalities, employing both class-agnostic and class-specific alignments that are iteratively refined to handle the expanding vocabulary of objects. Besides, 2D box guidance boosts the classification accuracy against complex background noises, which is coined as Box-DCMA. Extensive evaluation demonstrates the superiority of CoDAv2. CoDAv2 outperforms the best-performing method by a large margin (APNovel of 9.17 vs. 3.61 on SUN-RGBD and 9.12 vs. 3.74 on ScanNetv2). Source code and pre-trained models are available at the GitHub project page. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Few Shot Contrastive Spectral Clustering with Meta Learning and Neighbor Mining

Few Shot Contrastive Spectral Clustering with Meta Learning ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Nongxiao Wang Xulun Ye Jieyu Zhao Qing Wang Department of Computer Science and Technology Ningbo University China Key Lab of Mobile Network Application Technology of Zhejiang Province Ningbo China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Spectral clustering is widely used in the field of unsupervised learning and has been successfully applied in various data analysis tasks. However, in practical scenarios, it’s common to get few labeled data at low cost, resulting limited labeled data in few categories, while the rest are completely unlabeled. Relying solely on the unsupervised clustering framework can’t leverage the existing supervised information. Using a supervised meta-learning paradigm alone poses challenges when dealing with the majority categories of completely unlabeled data. Therefore, we propose a few-shot contrastive spectral clustering combined with meta learning and neighbor mining framework (FCSMN). First we leverage few shot labeled information to obtain a meta-learning feature embedding with strong generalization capabilities, and acquire semantic feature spaces using an unsupervised contrastive learning model. Then, we mine neighbors in these two feature spaces separately and align them. Next, we jointly reinforce intra-cluster compactness and inter-cluster separability at both instance and cluster level. To satisfies the conditions of spectral clustering, we impose orthogonality constraints at the last layer. Experiments on three datasets demonstrate the effectiveness our proposed method.

关键词： Metalearning Costs Semantics Neural networks Contrastive learning Data augmentation Data mining

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures 27

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Agarwal, Saurabh Kumar, Rajeev Devi, Abha Jung, Ki-Hyun Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Blockchain Technology Research Lab Department of Computer Science and Engineering Delhi Technological University New Delhi110042 India Department of Software Convergence Andong National University Gyeongbuk36729 Korea Republic of College Uttar Pradesh Sikandrabad India

ISBN: (纸本)9791188428137

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics;hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fake detection

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Graph Threading with Turn Costs

arXiv

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arXiv 2024年

作者： Demaine, Erik D. Kirkpatrick, Yael Lin, Rebecca Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology United States Department of Mathematics Massachusetts Institute of Technology United States

How should we thread a single string through a set of tubes so that pulling the string taut self-assembles the tubes into a desired graph? While prior work [ITCS 2024] solves this problem with the goal of minimizing the length of string, we study here the objective of minimizing the total turn cost. The frictional force required to pull the string through the tubes grows exponentially with the total absolute turn angles (by the Capstan equation), so this metric often dominates the friction in real-world applications such as deployable structures. We show that minimum-turn threading is NP-hard, even for graphs of maximum degree 4, and even when restricted to some special cases of threading. On the other hand, we show that these special cases can in fact be solved efficiently for graphs of maximum degree 3, thereby fully characterizing their dependence on maximum degree. We further provide polynomial-time exact and approximation algorithms for variants of turn-cost threading: restricting to threading each edge exactly twice, and on rectangular grid graphs. © 2024, CC BY.

关键词： Computational complexity

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RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads 3

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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3rd International Conference on Electrical Machines and Drives, ICEMD 2023

作者： Eikani, Alireza Amirkhani, Mohammad Jafari, Hossein Vaez-Zadeh, Sadegh Mirsalim, Mojtaba Khaburi, Davood Arab University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency. © 2023 IEEE.

关键词： Energy transfer

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