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33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021

作者： Chen, Qi Yan, Rong Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)1891706527

Influence Maximization (IM) problem has been attracted considerable interest and attention in last decades. However, the centrality algorithm-based methods were with low time complexity but made the acceptability of diffusion vaguely. The main purpose of our work is to select the influential nodes according to the available budget to maximize the impact coverage. Based on the traditional independent cascade model, this paper mainly solves the IM problem, designs two effective PRTH algorithms based on PageRank and propagation probability threshold, and combines PageRank of PRTH processed graph with degree discount algorithm to get an algorithm named PRDD. Experiments on four datasets show that the two algorithms have better performance than the existing algorithms in the aspect of influence diffusion. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Probability

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Electromigration of Backside Power Delivery Networks for PPA-Reliability Tradeoffs at N2 Node

Electromigration of Backside Power Delivery Networks for PPA...

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International Electron Devices Meeting (IEDM)

作者： Linlin Cai Yutao Chen Haoyu Zhang Jianwen Lin Binyu Yin Wangyong Chen School of Microelectronics Science and Technology Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China

Electromigartion (EM) reliability of backside power delivery network (BSPDN) using three kinds of techniques (BPR, TSVM and BSC) is comprehensively investigated by proposed electro-thermal-stress fully coupled EM simulation method. The physical behaviors of metal atom migration, vacancy generation and void nucleation are reproduced to offer the understanding of microscopic void evolutions. The obtained position of EM failure and resistance degradation show a well agreement with the experiments in different metals. By studying the time to failure (TTF) of various BSPDNs connected to power lines (GND, VDD) with alternative metal materials (W, Ru, Mo), valuable insights can be gained to mitigate potential failure risks and enhance the EM reliability of advanced interconnects technology. The tradeoffs of power-performance-area-reliability (PPAR) are predicted in different BSPDNs at N2 node.

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BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind

arXiv

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arXiv 2024年

作者： Mao, Yuanyuan Lin, Xin Ni, Qin He, Liang Shanghai Key Laboratory of Multidimensional Information Processing ECNU Shanghai China Department of Computer Science and Technology East China Normal University China Key Laboratory of Multilingual Education with AI Shanghai International Studies University China

As a foundational component of cognitive intelligence, theory of mind (ToM) can make AI more closely resemble human thought processes, thereby enhancing their interaction and collaboration with human. In particular, it can significantly improve a model’s comprehension of videos in complex scenes. However, current video question answer (VideoQA) datasets focus on studying causal reasoning within events, few of them genuinely incorporating human ToM. Consequently, there is a lack of development in ToM reasoning tasks within the area of VideoQA. This paper presents BDIQA, the first benchmark to explore the cognitive reasoning capabilities of VideoQA models in the context of ToM. BDIQA is inspired by the cognitive development of children’s ToM and addresses the current deficiencies in machine ToM within datasets and tasks. Specifically, it offers tasks at two difficulty levels, assessing Belief, Desire and Intention (BDI) reasoning in both simple and complex scenarios. We conduct evaluations on several mainstream methods of VideoQA and diagnose their capabilities with zero-shot, few-shot and supervised learning. We find that the performance of pre-trained models on cognitive reasoning tasks remains unsatisfactory. To counter this challenge, we undertake thorough analysis and experimentation, ultimately presenting two guidelines to enhance cognitive reasoning derived from ablation analysis. © 2024, CC0.

关键词： Zero-shot learning

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Achieving Distributed and Privacy-Preserving Cross-Chain Transactions in Account-Model Blockchain Systems

Achieving Distributed and Privacy-Preserving Cross-Chain Tra...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Zhang, Chuan Wang, Weijie Zhang, Weiting Nie, Jiangtian Liang, Jinwen Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China Beijing Institute of Technology School of Computer Science & Technology Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Nanyang Technological University School of Computer Science and Engineering Singapore Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350333336

Cross-Chain is an emerging technology to enable transaction circulation across different blockchain systems, breaking down transaction isolation among decentralized ecosystems. Existing cross-chain solutions are often constructed based on centralized trusted third parties, which introduce additional threats to users' identities due to potentially compromised risks on the centralized third parties. To exclude the single-point compromised risk in centralized systems, this paper focuses on a fully decentralized scenario and develops a distributed cross-chain transaction scheme, which enables cross-chain transaction exchanges while ensuring the unlinkability between payees and payers. Specifically, we first propose a cross-chain cryptocurrency exchange scheme by leveraging Ethereum's smart contracts, which achieves transaction exchanges in decentralized settings. Then, we leverage zero-knowledge succinct non-interactive argument of knowledge (i.e., zk-SNARKs) to explore a privacy-preserving method for cross-chain transactions in account-model blockchain systems, such that the unlinkability between payees and payers and the privacy of cross-chain participants can be guaranteed. Security analysis and experimental evaluations demonstrate that the proposed scheme achieves the security goal at acceptable costs. © 2023 IEEE.

关键词： Smart contract

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Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detection

arXiv

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arXiv 2024年

作者： Lin, Yuzhen Song, Wentang Li, Bin Li, Yuezun Ni, Jiangqun Chen, Han Li, Qiushi Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security SZU-AFS Joint Innovation Center for AI Technology Shenzhen University Shenzhen China College of Computer Science and Technology Ocean University of China Qingdao China School of Cyber Science and Technology Sun Yat-Sen University Department of New Networks Peng Cheng Laboratory Shenzhen China

Previous studies in deepfake detection have shown promising results when testing face forgeries from the same dataset as the training. However, the problem remains challenging when one tries to generalize the detector to forgeries from unseen datasets and created by unseen methods. In this work, we present a novel general deepfake detection method, called Curricular Dynamic Forgery Augmentation (CDFA), which jointly trains a deepfake detector with a forgery augmentation policy network. Unlike the previous works, we propose to progressively apply forgery augmentations following a monotonic curriculum during the training. We further propose a dynamic forgery searching strategy to select one suitable forgery augmentation operation for each image varying between training stages, producing a forgery augmentation policy optimized for better generalization. In addition, we propose a novel forgery augmentation named self-shifted blending image to simply imitate the temporal inconsistency of deepfake generation. Comprehensive experiments show that CDFA can significantly improve both cross-datasets and cross-manipulations performances of various naive deepfake detectors in a plug-and-play way, and make them attain superior performances over the existing methods in several benchmark datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Curricula

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Generative Multi-View HRRP Recognition Based on Cascade Generation and Fusion Network

Generative Multi-View HRRP Recognition Based on Cascade Gene...

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2024 International Radar Conference, RADAR 2024

作者： Zhou, Qiang Yu, Bingqian Wang, Yanhua Zhang, Liang Zheng, Le Zou, Difan Zhang, Xin Beijing Institute of Technology School of Information and Electronics Beijing 100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing 100081 China University of Hong Kong Department of Computer Science Hongkong Hong Kong University of Hong Kong HKU Musketeers Foundation Institute of Data Science Hongkong Hong Kong

ISBN: (纸本)9798350362381

High-resolution range profile (HRRP) is critical for radar target recognition. However, HRRP data for non-cooperative targets are often sparsely collected, leading to limited performance of HRRP target recognition methods. To address the issue of sparse view target recognition, we propose a generative multi-view HRRP recognition (GMVR) model. Firstly, by leveraging the pattern learning and generation ability of deep generative models (DGMs), we propose a viewpoint feature expansion module (VFEM) to generate multi-view features adjacent to sparse viewpoints. Subsequently, based on feature-level fusion, we proposed a multi-view feature fusion module (MVFM). Aided by attention mechanisms, the generated features can be adaptively integrated, thereby reducing the decision bias caused by single-viewpoint HRRP samples. Additionally, we employ transfer learning to mitigate the small sample problem caused by sparse viewpoints. Experimental results demonstrate that the proposed method outperforms various comparison methods, achieving optimal performance. © 2024 IEEE.

关键词： deep generative models (DGMs) high-resolution range profile (HRRP) multi-view recognition transfer learning

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Texture Mapping Based on Planar Projection Mapping in fully analytic polygon-based computer generated holography

Texture Mapping Based on Planar Projection Mapping in fully ...

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Frontiers in Optics, FiO 2022

作者： Fu, Qingyang Zhang, Yaping Zhang, Bing Poon, Ting-Chung Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Yunnan Kunming650500 China Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

We propose the use of planar projection mapping to texture fully analytic 3D affine transformed computer-generated holograms. Simulation results show that this is an effective texture mapping method. © 2022 The A...

ISBN: (纸本)9781557528209

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Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

arXiv

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arXiv 2023年

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Multilingual Pre-training Model-Assisted Contrastive Learning Neural Machine Translation

Multilingual Pre-training Model-Assisted Contrastive Learnin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuo Sun Hong–xu Hou Zong–heng Yang Yi–song Wang National& Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

Since pre-training and fine-tuning have been a successful paradigm in Natural Language processing (NLP), this paper adopts the SOTA pre-training model-CeMAT as a strong assistant for low-resource ethnic language translation tasks. Aiming at the exposure bias problem in the fine-tuning process, we use the contrastive learning framework and propose a new contrastive examples generation method, which uses self- generated predictions as contrastive examples to expose the model to errors during inference. Moreover, in order to effectively utilize the limited bilingual data in low-resource tasks, this paper proposes a dynamic training strategy to fine-tune the model, and refines the model step by step by taking word embedding norm and uncertainty as the criteria of evaluate data and model respectively. Experimental results demonstrate that our method significantly improves the quality compared to the baselines, which fully verifies the effectiveness.

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of information technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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