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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Feng, Bingwen Liu, Zhiquan Wei, Kaimin Lu, Wei Lin, Yuchun College of Information Science and Technology Jinan University Guangzhou510632 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen518060 China School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangzhou Institute of Science and Technology Guangzhou510006 China

ISBN: (纸本)9783030694487

This paper presents a type of variable rate syndrome-trellis codes (VR-STC) for bursty channels. It can embed message bits with two different embedding rates. In the embedding, a cover vector is sliced into segments, and the embedding rates for each segment depend on the local channel distribution. The parities of stego segments are exploited to indicate the selected embedding rates according to a mapping function between parities and embedding rates. The core of the VR-STC is the parity-aware encoder, which can simultaneously output two candidate stego segments with different embedding rates and opposite parities, either of which can be used to constitute the final stego vector. A Viterbi algorithm is also suggested to find the closed stego segments. Besides, the mapping function between parities and embedding rates is designed by minimizing the embedding costs on a down sampled version of the cover vector. It can further improve the undetectability of the VR-STC. Experimental results on artificial signals and binary images suggest that the proposed VR-STC can provide high success rate of embedding and reduce the embedding cost on bursty channels. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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A PRESTO-based parallel pulsar search pipeline used for FAST drift scan data

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Research in Astronomy and Astrophysics 2020年第6期20卷 215-222页

作者： Qiu-Yu Yu Zhi-Chen Pan Lei Qian Shen Wang You-Ling Yue Meng-Lin Huang Qiao-Li Hao Shan-Ping You Bo Peng Yan Zhu Lei Zhang Zhi-Jie Liu Guizhou Normal University Key Laboratory of Information and Computing ScienceGuiyang 550001China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of In formation Engineering Southwest University of Scie nee and TechnologyMianyang 621010China School of Physics University of Electronic Science and Technology of ChinaChengdu 610054China

We developed a pulsar search pipeline based on PulsaR Exploration and Search TOolkit(PRESTO).This pipeline simply runs dedispersion,Fast Fourier Transform(FFT)and acceleration search in process-level parallel to shorten the processing *** two parallel strategies,the pipeline can highly shorten the processing time in both normal searches and acceleration *** pipeline was first tested with Parkes Multibeam Pulsar Survery(PMPS)data and discovered two new faint ***,it was successfully applied in processing the Five-hundred-meter Aperture Spherical radio Telescope(FAST)drift scan data with tens of new pulsar discoveries up to *** pipeline is only CPU-based and can be easily and quickly deployed in computing nodes for testing purposes or data processing.

关键词： methods data analysis-pulsars general-star neutron

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Cosh-DiT: Co-Speech Gesture Video Synthesis via Hybrid Audio-Visual Diffusion Transformers

arXiv

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arXiv 2025年

作者： Sun, Yasheng Xu, Zhiliang Zhou, Hang Guan, Jiazhi Yang, Quanwei Wang, Kaisiyuan Liang, Borong Li, Yingying Feng, Haocheng Wang, Jingdong Liu, Ziwei Hideki, Koike Center of Excellence for Generative AI King Abdullah University of Science and Technology Jeddah Thuwal23955-6900 Saudi Arabia Baidu VIS Shangdi 10th Street Beijing100085 China School of Computing Tokyo Institute of Technology Ookayama Meguro-ku Tokyo152-8550 Japan Department of Computer Science and Technology Tsinghua University Haidian District Shuangqing Road Beijing100084 China Department of Electronic Engineering and Information Science University of Science and Technology of China Baohe District JinZhai Road Anhui Hefei230026 China S-Lab Nanyang Technological University 50 Nanyang Avenue 639798 Singapore

Co-speech gesture video synthesis is a challenging task that requires both probabilistic modeling of human gestures and the synthesis of realistic images that align with the rhythmic nuances of speech. To address these challenges, we propose Cosh-DiT, a Co-speech gesture video system with hybrid Diffusion Transformers that perform audio-to-motion and motion-to-video synthesis using discrete and continuous diffusion modeling, respectively. First, we introduce an audio Diffusion Transformer (Cosh-DiT-A) to synthesize expressive gesture dynamics synchronized with speech rhythms. To capture upper body, facial, and hand movement priors, we employ vector-quantized variational autoencoders (VQ-VAEs) to jointly learn their dependencies within a discrete latent space. Then, for realistic video synthesis conditioned on the generated speech-driven motion, we design a visual Diffusion Transformer (Cosh-DiT-V) that effectively integrates spatial and temporal contexts. Extensive experiments demonstrate that our framework consistently generates lifelike videos with expressive facial expressions and natural, smooth gestures that align seamlessly with speech. © 2025, CC BY.

关键词： Vector quantization

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Federated Learning for 6G Communications:Challenges,Methods,and Future Directions

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China Communications 2020年第9期17卷 105-118页

作者： Yi Liu Xingliang Yuan Zehui Xiong Jiawen Kang Xiaofei Wang Dusit Niyato School of Data Science of Technology Heilongjiang UniversityHarbinChina Faculty of Information Technology Monash UniversityAustralia Alibaba-NTU Joint Research Institute and also School of Computer Science and Engineering NTUSingapore Energy Research Institute Nanyang Technological UniversitySingapore College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Engineering NTUSingapore

As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G *** is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale ***,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy *** learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in *** this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for *** then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.

关键词： 6G communication federated learning security and privacy protection

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

arXiv

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Fractional Fourier Transform-Based OFDM System with low complexity equalizer for Underwater Acoustic Communications

Fractional Fourier Transform-Based OFDM System with low comp...

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information technology (ACIT)

作者： Hani Attar Jafar Ababneh Ali Rachini Ramy M. Bahy Mohamed Hafez Ahmed Solyman Faculty of Engineering Zarqa University Zarqa Jordan Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science and Information Technology Holy Spirit University of Kaslik (USEK) Jounieh Lebanon Technical Research Center Cairo Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Underwater communication systems have garnered significant interest due to the limitations of the wireless spectrum and the growing need for sustained, covert communication capabilities. However, existing systems face significant challenges from doubly dispersive channels that cause time and frequency fading, disrupting the orthogonality of conventional OFDM subcarriers and leading to severe inter-symbol interference (ISI), even with complex equalizers like MMSE. This paper proposes a Fractional Fourier Transform (FrFT)-based OFDM system, offering better adaptability in the time-frequency domain. The FrFT-OFDM system effectively concentrates power around the diagonal of the channel matrix, mitigating Doppler shifts and multipath fading, thus enhancing spectral efficiency and robustness. A low-complexity equalizer is introduced, reducing computational demands while maintaining high performance, ensuring practical real-time application in underwater environments. Simulation results show that the proposed DFrFT-OFDM system with the optimized equalizer surpasses traditional OFDM under severe channel dispersion, demonstrating improved bit error rates and signal-to-noise ratios by about 3 db. This confirms its capability for reliable underwater communication with reduced computational overhead by about 90% comparing the OFDM with MMSA and DFrFT-OFDM with bande MMSA, marking a significant step forward for high-performance data transmission in challenging aquatic environments.

关键词： Fading channels Underwater communication Time-frequency analysis Equalizers OFDM Simulation Bit error rate Underwater acoustics Dispersion Signal to noise ratio

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Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans

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Computational Intelligence and Neuroscience 2022年第0期2022卷 7124199页

作者： Kaushik, Bhavana Koundal, Deepika Goel, Neelam Zaguia, Atef Belay, Assaye Turabieh, Hamza School of Computer Science University of Petroleum & Energy Studies Bidholi Dehradun India University Institute of Engineering and Technology Panjab University Chandigarh India Department of Information Technology College of Computing and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Statistics Mizan-Tepi University Tepi Ethiopia

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs. © 2022 Bhavana Kaushik et al.

关键词： Diagnosis

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Proactive Query Expansion for Streaming Data Using External Sources

Proactive Query Expansion for Streaming Data Using External ...

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IEEE International Conference on Big Data

作者： Farah Alshanik Amy Apon Yuheng Du Alexander Herzog Ilya Safro Department of Computer Science Jordan University of Science and Technology Irbid Jordan School of Computing Clemson University Clemson SC Department of Computer and Information Sciences University of Delaware Newark DE

ISBN: (纸本)9781665480468

Queries used to draw data from high-volume, high-velocity social media data streams, such as Twitter, typically require a set of keywords to filter the data. When topics and conversations change rapidly, initial keywords may become outdated and irrelevant, which may result in incomplete data. We propose a novel technique that improves data collection from social media streams in two ways. First, we develop a query expansion method that identifies and adds emergent keywords to the initial query, which makes the data collection a dynamic process that adapts to changes in social conversations. Second, we develop a "predictive query expansion" method that combines keywords from the streams with external data sources, which enables the construction of new queries that effectively capture emergent events that a user may not have anticipated when initiating the data collection stream. We demonstrate the effectiveness of our approach with an analysis of more than 20.5 million Twitter messages related to the 2015 Baltimore protests. We use newspaper archives as an external data source from which we collect keywords to expand the queries built from the primary ***: https://***/FarahAlshanik/QE

关键词： Social networking (online) Soft sensors Blogs Oral communication Big Data

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Malay lexical simplification model for non-native speaker 5

Malay lexical simplification model for non-native speaker

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5th International Conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Omar, Salehah Bakar, Juhaida Abu Nadzir, Maslinda Mohd Harun, Nor Hazlyna Yusoff, Nooraini Sultan Abdul Halim Mu'adzam Shah Polytechnic Department of Information Technology and Communication Kedah Jitra06000 Malaysia Universiti Utara Malaysia Data Science Research Lab School of Computing Kedah Sintok06010 Malaysia Institute for for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia

ISBN: (纸本)9781665495585

Vocabulary is an important language skill that can affect a person's understanding of a sentence. Thus, lexical simplification is the task of converting difficult words into simpler words. It is to make it easier for the reader to understand the sentences. The biggest challenge in lexical simplification is to simplify the words needed without changing the meaning of the sentence. Past studies have shown that there are weaknesses in this task, where simple words are also identified as complex words. This issue has led to the simplification of unnecessary words. The purpose of the study is to produce a complex word identification model for the Malay language into words that are more easily understood by nonnative speakers. Experiments was performed on the appropriate features to obtain the required results. Machine learning was used to ensure the results were more accurate. This study is a novelty in text simplification of the Malay language in the field of Natural Language Processing (NLP) and may be used as a preprocessing tool to improve other tasks in NLP. © 2022 IEEE.

关键词： Learning algorithms

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Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition

arXiv

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arXiv 2023年

作者： Ye, Weishan Zhang, Zhiguo Teng, Fei Zhang, Min Wang, Jianhong Ni, Dong Li, Fali Xu, Peng Liang, Zhen The School of Biomedical Engineering Medical School Shenzhen University Shenzhen518060 China The Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China The Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen518000 China The Marshall Laboratory of Biomedical Engineering Shenzhen518060 China The Peng Cheng Laboratory Shenzhen518055 China The Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen518000 China Shenzhen Mental Health Center Shenzhen Kangning Hospital Shenzhen China The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China China School of Life Science and Technology Center for Information in Medicine University of Electronic Science and Technology of China China

Electroencephalography (EEG) is an objective tool for emotion recognition with promising applications. However, the scarcity of labeled data remains a major challenge in this field, limiting the widespread use of EEG-based emotion recognition. In this paper, a semi-supervised Dual-stream Self-attentive Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition. The DS-AGC framework includes two parallel streams for extracting non-structural and structural EEG features. The non-structural stream incorporates a semi-supervised multi-domain adaptation method to alleviate distribution discrepancy among labeled source domain, unlabeled source domain, and unknown target domain. The structural stream develops a graph contrastive learning method to extract effective graph-based feature representation from multiple EEG channels in a semi-supervised manner. Further, a self-attentive fusion module is developed for feature fusion, sample selection, and emotion recognition, which highlights EEG features more relevant to emotions and data samples in the labeled source domain that are closer to the target domain. Extensive experiments are conducted on three benchmark databases (SEED, SEED-IV and SEED-V) using a semi-supervised cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show that the proposed model outperforms existing methods under different incomplete label conditions with an average improvement of 2.17%, which demonstrates its effectiveness in addressing the label scarcity problem in cross-subject EEG-based emotion recognition. The source code is available at https://***/Vesan-yws/DS-AGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Self-supervised learning

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