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BACON: Boundary-guided Polypharmacy Side Effect Prediction via Integrating Molecular Structures and Biochemical Information

BACON: Boundary-guided Polypharmacy Side Effect Prediction v...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Yike Ma, Huifang Gao, Zihao Li, Zhixin Chang, Liang Northwest Normal University College of Computer Science and Engineering Lanzhou China Ministry of Education Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Guangxi Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guangxi Guilin China

ISBN: (纸本)9798350337488

Polypharmacy side effect prediction is a vital task in healthcare machine learning, aiming to predict the occurrence of multiple side effects when patients take drugs together. Existing researches mainly follow the deep encoder-decoder paradigm and suffer from the following two limitations: 1) The encoder simply combines inadequate information about drugs;2) The decoder fails to capture dependencies among different side effects, thus hindering accurate prediction. To overcome the aforementioned limitations, we propose a boundary-guided polypharmacy side effect prediction method (BACON). Our framework constructs two complementary views based on drug's chemical substructure and biochemical features and enhances drug representations via contrastive learning. The decoder incorporates a boundary-guided strategy to capture drug interaction dependencies for optimizing polypharmacy side effect prediction. Experimental results demonstrate BACON superiority over SOTA models in accurately predicting drug side effect events. © 2023 IEEE.

关键词： Boundary-guided Strategy Dual-view Contrastive Learning Polypharmacy Side Effect Prediction

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FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Language-Image Pre-Training 24

FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Lang...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Yudong Hou, Xianxu Dezhi, Zheng Shen, Linlin Zhao, Zhe School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Tencent AI Lab Beijing China

ISBN: (纸本)9798400706868

While significant progress has been made in multi-modal learning driven by large-scale image-text datasets, there is still a noticeable gap in the availability of such datasets within the facial domain. To facilitate and advance the field of facial representation learning, we present FLIP-80M, a large-scale visual-linguistic dataset comprising over 80 million face images paired with text descriptions. FLIP-80M is constructed by leveraging the large openly available image-text-pair dataset LAION-5B and a mixed-method approach to filter face-related pairs from both visual and linguistic perspectives. Our curation process involves face detection, face caption classification, text de-noising, and synthesis-based image augmentation. As a result, FLIP-80M stands as the largest face-text dataset to date. To evaluate the potential of our dataset, we fine-tune the CLIP model using the proposed FLIP-80M, to create FLIP (Facial Language-Image Pretraining) and assess its representation capabilities across various downstream tasks. Our experiments demonstrate that our FLIP model achieves state-of-the-art results in a range of face analysis tasks, including face parsing, face alignment, and face attribute classification. The dataset and models are available at https://***/ydli-ai/FLIP. © 2024 ACM.

关键词： Face recognition

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Small object detection algorithm for UAV images based on YOLOv5

Small object detection algorithm for UAV images based on YOL...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Song, Nan Wang, Ying Liu, Tianxu Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475004 China Software Engineering Intelligent Information Processing Innovation Base-Subject Innovation Base of Henan Higher Universities Henan University Kaifeng475004 China Henan Provincial Communications and Transportation Dispatch and Command Center Zhengzhou450000 China

ISBN: (纸本)9781510681514

In the field of deep learning-based object detection algorithms, the YOLO algorithm has attracted a lot of attention due to its advantages in speed and accuracy. However, there are problems such as low detection speed and poor detection accuracy in the application of UAV images. For this reason, an improved small object detection algorithm MTF-YOLOv5 based on the YOLOv5 algorithm is proposed. Firstly, MobileNet V3 is used to replace the main network to reduce the amount of network parameters and improve the running speed. Secondly, a small object detection layer is added to the original network structure to enhance the detection ability for small objects. Finally, the FocalModulation is introduced to replace the SPP module to improve accuracy. The improved algorithm is tested on the processed TinyPerson dataset. Experimental results show that the mean average precision (mAP) on the YOLOv5s algorithm improved by 4.1%, and the FPS increased from 86 to 136, improving the speed by 51%. While maintaining detection speed, the updated algorithm successfully raises detection accuracy. © 2024 SPIE.

关键词： Object detection

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Bingshu Lin, Jia Zhuang, Xiaodong Zhang, Guanghui Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi'an China Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665410205

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Heterogeneous Laplace Distribution Noise Background Modeling for Sperm Detection in Microscopic Images 9

Heterogeneous Laplace Distribution Noise Background Modeling...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Tian, Manqing Zhao, Yang Zeng, Ni Kang, Xiaomei Pei, Jihong Yang, Xuan Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Huazhong University of Science and Technology Tongji Medical College Department of Reproductive Maternal and Child Health Hospital of Hubei Province Wuhan China

ISBN: (纸本)9798350317251

Semen quality assessment is a very important tool for the timely detection and treatment of infertility disorders. Many artificial intelligence methods for sperm image analysis have emerged in recent years. Sperm detection in sperm microscopy is the first step in semen quality assessment. However, the acquisition of sperm electron microscopy images can produce a variety of different background lighting situations. The performance of established methods is susceptible to changes in background illumination. Therefore, a sperm detection method with heterogeneous Laplacian distribution noise background modeling (HLDNBM) is proposed. In this paper the method uses background illumination surface fitting to eliminate the effect of illumination in a single sperm image, and realizes the segmentation of sperm head and sperm tail with different grayscale by upper and lower segmentation threshold surfaces to achieve complete sperm image segmentation and multi-target detection in a single image. This method effectively avoids the degradation of target detection performance caused by sudden changes in illumination in other background modeling methods. Moreover, this method shows strong adaptability for images with different lighting backgrounds, and is friendly to small datasets as well. The experimental results show that the accuracy and sensitivity of the algorithm for sperm detection in images with different grayscale characteristics are very promising, with high universality and robustness. © 2023 IEEE.

关键词： Lighting

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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Poster: Towards Accurate and Fast Federated Learning in End-Edge-Cloud Orchestrated Networks 43

Poster: Towards Accurate and Fast Federated Learning in End-...

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43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023

作者： Li, Mingze Sun, Peng Zhou, Huan Zhao, Liang Liu, Xuxun Leung, Victor C. M. College of Computer and Information Technology China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electronic and Information Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350339864

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks. © 2023 IEEE.

关键词： Synchronization

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Research on Optimization of Nuclear Power Security DCS Gateway Encryption Communication Based on OPC UA Protocol 9th

Research on Optimization of Nuclear Power Security DCS Gatew...

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9th International Conference on Energy engineering and Environmental engineering, ICEEEE 2022

作者： Zhou, Pengjun Chen, Zhi Jiang, Wei Liu, Jie Zhang, Yan Zhong, Ke Wu, Zhiqiang School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (纸本)9783031302329

Nuclear power safety grade DCS gateway is an important hub for information transmission between safety grade equipment and non-safety grade equipment, and its security is very important in the transmission process. At present, the communication protocol used by the Chinese nuclear power security grade DCS gateway does not use data encryption in the data transmission process. However, the signature certificate, user authentication, access control and data encryption transmission of OPC UA protocol have more advantages in ensuring the data security of nuclear power security grade DCS gateway, which helps to meet the technical requirements for nuclear power security grade DCS information security. Based on the cryptography theory, this paper analyzes the security and efficiency of the original RSA encryption algorithm of the OPC UA protocol, and concludes that the encryption efficiency of the RSA algorithm is poor, which leads to the application of the OPC UA protocol to the nuclear power security grade DCS gateway communication process can not meet the time requirements of data transmission, so it is necessary to optimize and improve the communication process of the OPC UA protocol applied to the nuclear power security grade DCS gateway. This paper optimizes and improves the encryption transmission in the communication process of the nuclear power security grade DCS gateway of OPC UA protocol, and proposes a hybrid encryption algorithm of SM2 and AES to replace the original encryption method in OPC UA protocol. Through experiments, it proves that the communication optimization method improves the communication efficiency of the original protocol, and achieves the efficiency index of nuclear power security grade DCS gateway for data transmission. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform 1

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9th International Conference on Energy engineering and Environmental engineering, ICEEEE 2022

作者： Bai, Yongfei Wu, Zhiqiang Liu, Jie Jiang, Wei Zhong, Ke Han, Wenxing Chen, Zhi School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (数字)9783031302336

ISBN: (纸本)9783031302329

At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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