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Lecture Notes in Computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2024年 14640 LNAI卷 v-vi页

作者： Fontenla-Romero, Óscar Alonso-Betanzos, Amparo Guijarro-Berdiñas, Bertha Hernández-Pereira, Elena Bolón-Canedo, Verónica Sánchez-Maroño, Noelia Pérez-Sánchez, Beatriz Camacho, David Rabuñal, Juan R. Ojeda-Aciego, Manuel Medina, Jesús Riquelme, José C. Troncoso, Alicia University of A Coruña La Coruña A Coruña Spain Technical University of Madrid Madrid Spain University of Malaga Malaga Spain University of Cádiz Cádiz Spain University of Seville Seville Spain Data Science and Big Data Lab Pablo de Olavi de University Seville Spain

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Objective Hand Complexity Comparison between Two Mandarin Chinese Cued Speech Systems

Objective Hand Complexity Comparison between Two Mandarin Ch...

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International Symposium on Chinese Spoken Language Processing

作者： Li Liu Gang Feng Xiaoxi Ren Xianping Ma Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble France Laboratoire IADI INSERM université de lorraine Nancy France School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350397970

Recently, a pilot Mandarin Chinese Cued Speech (MCCS) system, called MCCS-1 was proposed with a main characteristic that each vowel is coded by only one specific hand position, without using any hand slides to code diphthongs. Indeed, hand slides are also used in some other languages of CS to code diphthongs. In order to demonstrate that the MCCS-1 system possesses real advantages over systems using hand slides, in this work, we first propose a novel MCCS-2 by introducing hand slides to code diphthongs, and a “push out move” for ending consonants [n] or [ng] of nasalized vowels. Then, we present a multi-parameter hand complexity measure method to compare MCCS-1 and MCCS-2 by measuring three kinematic parameters, which are the time duration of words realization, hand move trajectory length and average speed of hand movements. Moreover, the first MCCS corpus for these two systems is recorded by three speakers specifically for this work. A statistical analysis of this database shows a superior performance of MCCS-1 since it costs less time and takes a shorter hand move trajectory length than MCCS-2. A strong correlation is observed between the complexity of hand movement and the time duration of word realization.

关键词： Codes Costs Speech coding Statistical analysis databases Kinematics Length measurement

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NAS-FCOS: Efficient search for object detection architectures

arXiv

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arXiv 2021年

作者： Wang, Ning Gao, Yang Chen, Hao Wang, Peng Tian, Zhi Shen, Chunhua Zhang, Yanning School of Computer Science Northwestern Polytechnical University China National Engineering Lab for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China The University of Adelaide Australia Monash University Australia

Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is noteworthy is that as of now, object detection is less touched by NAS algorithms despite its significant importance in computer vision. To the best of our knowledge, most of the recent NAS studies on object detection tasks fail to satisfactorily strike a balance between performance and efficiency of the resulting models, let alone the excessive amount of computational resources cost by those algorithms. Here we propose an efficient method to obtain better object detectors by searching for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely, FCOS [36], using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs. The discovered architectures surpass Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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MM-trafficEvent: An Interactive Incident Retrieval System for First-view Travel-log data

MM-trafficEvent: An Interactive Incident Retrieval System fo...

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IEEE International Conference on big data

作者： Minh-Son Dao Dinh-Duy Pham Manh-Phu Nguyen Thanh-Binh Nguyen Koji Zettu Big Data Integration Research Center National Institute of Information and Communications Technology (NICT) Tokyo Japan AISIA Research Lab University of Science (VNU-HCMUS) Vietnam National University HCM City Vietnam

ISBN: (纸本)9781665445993

Dashcam video has become popular recently due to the safety of both individuals and communities. While an individual can have undeniable evidence for legal and insurance, communities can benefit from sharing these dashcam videos for further traffic education and criminal investigation. Moreover, relying on recent computer vision and AI development, few companies have launched the so-called AI dashcam that can alert drivers to near-risk accidents (e.g.., following distance detection, forward collision warning), forwarding to improving driver’s safety. However, even though dashcam videos create a driver’s travel log (i.e., traveling diary), little research focuses on creating a valuable and friendly tool to find any incident or event with few described sketches by users. Besides, most incident detection models have been built using a traditional supervised learning approach (i.e., collecting and labeling data for a new incident class). That prevents the quick and customized development of a new incident class. Inspired from these observations, we introduce an interactive incident detection and retrieval system for first-view travel-log data, namely MM-trafficEvent, that can (1) online defined-incident detection, (2) offline fine-grained incident retrieval for both defined and undefined incidents, (3) offline automatically new incident class creating using user’s queries. Moreover, the system gives promising results when being evaluated on several public datasets.

关键词： Semantics Supervised learning Detectors Search engines big data Transformers Generators

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Rethinking the trigger of backdoor attack

arXiv

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arXiv 2020年

作者： Li, Yiming Zhai, Tongqing Wu, Baoyuan Jiang, Yong Li, Zhifeng Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China Tencent AI Lab Shenzhen China

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger, while it performs well on benign samples. Currently, most of existing backdoor attacks adopted the setting of static trigger, i.e., triggers across the training and testing images follow the same appearance and are located in the same area. In this paper, we revisit this attack paradigm by analyzing the characteristics of the static trigger. We demonstrate that such an attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training. We further explore how to utilize this property for backdoor defense, and discuss how to alleviate such vulnerability of existing attacks. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

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IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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An Implementation of Face Recognition System with Face Presentation Attack Detection on Raspberry Pi

An Implementation of Face Recognition System with Face Prese...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Wenjun Wang Xiangkang Huang Xin Shu Shuping Li Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China School of Data Science and Information Engineering Guizhou Minzu University Guiyang China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

With the popularity of unlocking and payment of using the face-swiping function, the era of face recognition has arrived. However, the current face recognition technology can identify the identity of the face image but cannot accurately distinguish the authenticity of the input face on embedded devices. Recently, face recognition with face presentation attack detection has become a research hotspot. In this paper, we develop an intelligent face recognition system on Raspberry Pi to handle this issue. Specifically, we first add a face presentation attack detection process to form a three-stage face recognition method to realize face recognition with face anti-spoofing. Second, we select a face detection model, a face presentation attack detection model and a face recognition model suitable for Raspberry Pi, and transplant these models previously trained in the Python environment to the C++ environment. Then we integrate and deploy these three models on Raspberry Pi and use the NCNN inference framework for efficient reasoning within limited memory and computing resources. Finally, we implement the face recognition system and conduct real-world tests to verify the performance. The experimental results demonstrate that our system can be applied in practice, and can achieve good effects.

关键词： Face recognition Computational modeling Conferences Memory management C++ languages Cognition Organ transplantation

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TediGAN: Text-guided diverse face image generation and manipulation

arXiv

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arXiv 2020年

作者： Xia, Weihao Yang, Yujiu Xue, Jing-Hao Wu, Baoyuan Tsinghua Shenzhen International Graduate School Tsinghua University China Department of Statistical Science University College London United Kingdom School of Data Science Chinese University of Hongkong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. The instancelevel optimization is for identity preservation in manipulation. Our model can produce diverse and high-quality images with an unprecedented resolution at 10242. Using a control mechanism based on style-mixing, our Tedi- GAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels, with or without instance guidance. To facilitate text-guided multimodal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://***/weihaox/TediGAN. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information Technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning

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Artificial Cells, Blood Substitutes, and Biotechnology 2025年第1期53卷

作者： Kai Wei Liyun Yuan Yongsheng Ge Han Yu Guoping Zhao Guoqing Zhang Guohua Liu Children’s Hospital Affiliated to Shandong University Jinan ChinaJinan Children’s Hospital Jinan ChinaBio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Science Shanghai ChinaMedical AI Lab The First Hospital of Hebei Medical University Shijiazhuang China Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Science Shanghai China Children’s Hospital Affiliated to Shandong University Jinan ChinaJinan Children’s Hospital Jinan China Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Science Shanghai ChinaKey Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore macrophage-related diagnostic genes and GDM subtypes. For scRNA-seq data, cell clusters were annotated using the SingleR package and validated with marker gene expression profiles, while hdWGCNA analysis identified three gene modules related to macrophages. A diagnostic model for GDM derived from endothelial cell transcriptomes was constructed by employing a variety of machine learning ensemble algorithms, achieving an AUC of 0.887. The model identified five differentially expressed genes (ZEB2, MALAT1, HEBP1, AHSA1, and TTC3) as potential diagnostic biomarkers. The CB-DSNMF algorithm was proposed to identify two distinct GDM subtypes from RNA-seq data, revealing significant differences in biological behaviours. This algorithm outperformed other baselines in multiple clustering metrics. Mendelian randomisation analysis identified ZEB2 as a gene causally related to GDM risk. A transcription factor (TF)-gene regulatory network was constructed for these genes using the ENCODE database. The study highlights the importance of macrophages in GDM, provides a high-precision diagnostic model, and offers new insights into personalised treatment strategies, contributing to a better understanding of GDM pathophysiology.

关键词： Gestational diabetes mellitus macrophages non-negative matrix factorisation machine learning immune microenvironment

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