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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ye, Zijie Jia, Jia Wu, Haozhe Huang, Shuo Sun, Shikun Xing, Junliang Tsinghua University Department of Computer Science and Technology Beijing100084 China Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9781728163277

Synthesizing co-speech gestures is challenging because the mapping from speech to gesticulation is inherently non-deterministic. When giving talks, people conduct not only gentle and rhythmic motions but also abrupt and salient gesticulations. Most previous research efforts, however, ignore this nature of co-speech gestures and synthesize deterministic results, producing over-smoothed movements with limited expressiveness. To address this issue, we propose a new co-speech gesture generation approach that produces high-quality salient gesticulations. Specifically, we build a discrete motion representation (DMR) space to bridge the speech-gesture mapping and the gesture generation stages. The incorporation of DMR enables random sampling in motion space and avoids the over-smooth problem in speech-gesture mapping. Based on DMR, we devise a novel multi-modal co-speech gesture synthesis model with temporal attention (MCGT). MCGT explicitly models DMR's categorical distribution conditioned on the speech context, which captures complex context patterns and produces more salient gesticulations in sync with the context. In addition, we construct a new benchmark for evaluating salient motion quality in co-speech gestures, containing a large-scale co-speech gesture dataset with salient gesticulations. We also introduce a new metric, referred to as salient motion similarity, to evaluate the salient motion quality. Experiments demonstrate superior results from our approach over several competing baselines. © 2023 IEEE.

关键词： Co-speech gesture Discrete motion representation Temporal attention

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Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features

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Chinese Journal of Electronics 2022年第1期31卷 59-67页

作者： CHEN Beijing TAN Weijin WANG Yiting ZHAO Guoying Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology School of Computer Nanjing University of Information Science and Technology Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Warwick Manufacturing Group University of Warwick Center for Machine Vision and Signal Analysis University of Oulu

With the development of face image synthesis and generation technology based on generative adversarial networks(GANs), it has become a research hotspot to determine whether a given face image is natural or generated. However, the generalization capability of the existing algorithms is still to be improved. Therefore,this paper proposes a general algorithm. To do so, firstly,the learning on important local areas, containing many face key-points, is strengthened by combining the global and local features. Secondly, metric learning based on the Arc Face loss is applied to extract common and discriminative features. Finally, the extracted features are fed into the classification module to detect GAN-generated *** experiments are conducted on two publicly available natural datasets(Celeb A and FFHQ) and seven GANgenerated datasets. Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms. Moreover, the proposed algorithm is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.

关键词： feature extraction neural nets GAN-generated datasets local features metric learning generative adversarial networks face image GAN-generated faces generation technology learning (artificial intelligence) discriminative features GAN-generated face images face recognition face image synthesis generalization performance global features face key-points

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Leveraging Sharding-Based Hybrid Consensus for Blockchain

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computers, Materials & Continua 2024年第10期81卷 1215-1233页

作者： Hind Baageel Md Mahfuzur Rahman Department of Information&Computer Science King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center(IRC)for Intelligent Secure Systems King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The advent of blockchain technology has transformed traditional methods of information exchange,shifting reliance from centralized data centers to decentralized *** blockchain’s decentralization and security are strengths,traditional consensus mechanisms like Proof of Work(PoW)and Proof of Stake(PoS)face limitations in *** achieves decentralization and security but struggles with scalability as transaction volumes grow,while PoS enhances scalability,but risks centralization due to monopolization by high-stake ***,a recent advancement in blockchain technology,addresses scalability by partitioning the network into shards that process transactions independently,thereby improving throughput and reducing ***,cross-shard communication,essential for transactions involving multiple shards,introduces challenges in coordination and fault *** research introduces a shard-based hybrid consensus model,PoSW,which combines PoW and PoS to mitigate the limitations of both *** integrating PoW’s fairness with PoS’s scalability in a shard-based blockchain,the proposed model addresses key issues of scalability and *** evaluate the model against state-of-the-art consensus algorithms,including Monoxide and Practical Byzantine Fault Tolerance(PBFT).The results show that the proposed PoSW model reduces communication overhead compared to PBFT and improves resource utilization over *** addition to performance gains,the security analysis demonstrates that the PoSW model provides robust defense against common blockchain attacks such as the 51%and Sybil attacks,*** proposed approach is particularly suited for applications like decentralized finance(DeFi)and supply chain management,which require both high scalability and robust *** contributions of this research include the development of the PoSW hybrid consensus mechanism,its comparative evaluation with leading algorithms,and a thorough

关键词： Blockchain consensus algorithm decentralized network

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Automatic Bowel Preparation Assessment Using Deep Learning 26th

Automatic Bowel Preparation Assessment Using Deep Learning

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Haithami, Mahmood Salah Ahmed, Amr Liao, Iman Yi Altulea, Hamid Jalab School of Computer Science University of Nottingham Semenyih Malaysia School of Computer Science University of Nottingham Nottingham United Kingdom Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Dhi Qar Iraq

ISBN: (纸本)9783031376597

Bowel preparation is considered a critical step in colonoscopy. Manual bowel preparation assessment is time consuming and prone to human errors and biases. Automatic Bowel evaluation using machine/deep learning is a better and efficient alternative. Most of the relevant literature have focused on achieving high validation accuracy, where private handy-picked dataset does not reflect real-environment situation. Furthermore, treating a video dataset as a collection of individual frames may produce overestimated results. This is due to the fact a video contains nearly identical consecutive frames, hence, dividing them into training and validation sets yields two similar distributed datasets. Given a public dataset, Nerthus, we show empirically a significant drop in performance when a video dataset is treated as a collection of videos (depicting the real environment/context), instead of a collection of individual frames. We propose a model that utilizes both sequence and none-sequence (spatial) information within videos. The proposed model achieved on average 83% validation accuracy across 4 validation sets, whereas, the state-of-the-art models achieved on average a range of 66%–72% validation accuracy. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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CHART:Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning

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computers, Materials & Continua 2024年第12期81卷 4171-4194页

作者： Rashid Ahmad Asif Nawaz Ghulam Mustafa Tariq Ali Mehdi Tlija Mohammed AEl-Meligy Zohair Ahmed University Institute of Information Technology PMAS Arid Agriculture UniversityRawalpindi46000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Jadara University Research Center Jadara UniversityIrbid21110Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Science and Engineering Central South UniversityChangsha410083China

Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public *** methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the *** some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal *** research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these *** proposed methodology begins with comprehensive data collection from the police *** dataset includes detailed attributes such as crime type,location,time and demographic *** key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant *** Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various *** Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),*** designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime *** of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhanc

关键词： Crime hotspot heat map kernel density estimation(KDE) support vector machine(SVM)

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Video Polyp Segmentation: A Deep Learning Perspective

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Machine Intelligence research 2022年第6期19卷 531-549页

作者： Ge-Peng Ji Guobao Xiao Yu-Cheng Chou Deng-Ping Fan Kai Zhao Geng Chen Luc Van Gool Research School of Engineering Australian National UniversityCanberra 2601Australia College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Computer Vision Laboratory ETH ZürichZürich 8092Switzerland Department of Radiological Sciences University of CaliforniaLos Angeles 90095USA School of Computer Science and Engineering Northwestern Polytechnical UniversityXi′an 710072China

We present the first comprehensive video polyp segmentation(VPS)study in the deep learning *** the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation *** address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known *** provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and ***,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)*** global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS *** experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS ***,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based ***,we discuss several open issues and suggest possible research directions for the VPS *** project and dataset are publicly available at https://***/GewelsJI/VPS.

关键词： Video polyp segmentation(VPS) dataset self-attention colonoscopy abdomen

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International Conference on Machine vision, Image Processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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