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International Conference on Advanced Cloud and Big Data (CBD)

作者： Zhongyuan Kang Lu Zhang Zheng Chen Haiting Zhu Gaofeng He Mingwei Tang School of Computer Science Nanjing Audit University Nanjing China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China School of Engineering Audit Nanjing Audit University Nanjing China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Extracting and analyzing topics from the scientific literature is crucial for identifying the research hotspots, under-standing the developmental trajectory, and further revealing and predicting the evolutionary trends of a research field. Existing topic models like LDA and ETM can effectively extract topics from large text corpora. However, they struggle with incor-porating the domain-specific prior knowledge that is inherent and can be easily obtained in the scientific literature (e.g., the keywords in the paper), causing weekly targeted but over generic topic extraction and representation. To address this problem, we propose a Prior Attention Topic Model (PATM), which leverages inherent keywords in the scientific literature as prior knowledge for more targeted topic extracting and representation. Specifically, we first construct a prior attention weight matrix to guide the document embedding to pay more attention on the keywords, and then, a multi-view Variational Autoencoder (VAE) is utilized to separately encode different literature types, combining their unique characteristics while sharing one latent space. At last, the topic-word probability martix learned from the VAE are regarded as the topic representations. The experimental results and case study demonstrate that the proposed PATM is effective in extracting topics in the scientific literature, including papers and patents, and by analyzing these topics, we clearly reveal the thematic evolution of the research about emerging information technologies empowering auditing.

关键词： Patents Analytical models Solid modeling Big Data Solids Market research Data models Trajectory Data mining information technology

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Delving into the Estimation Shift of Batch Normalization in a network

arXiv

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arXiv 2022年

作者： Huang, Lei Zhou, Yi Wang, Tian Luo, Jie Liu, Xianglong SKLSDE Institute of Artificial Intelligence Beihang University Beijing China MOE Key Laboratory of Computer Network and Information Integration Southeast University China

Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics during inference. This paper focuses on investigating the estimation of population statistics. We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones. Our primary observation is that the estimation shift can be accumulated due to the stack of BN in a network, which has detriment effects for the test performance. We further find a batch-free normalization (BFN) can block such an accumulation of estimation shift. These observations motivate our design of XBNBlock that replace one BN with BFN in the bottleneck block of residual-style networks. Experiments on the ImageNet and COCO benchmarks show that XBNBlock consistently improves the performance of different architectures, including ResNet and ResNeXt, by a significant margin and seems to be more robust to distribution shift. Copyright © 2022, The Authors. All rights reserved.

关键词： Population statistics

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Language-Guided Face Animation by Recurrent StyleGAN-based Generator

arXiv

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arXiv 2022年

作者： Hang, Tiankai Yang, Huan Liu, Bei Fu, Jianlong Geng, Xin Guo, Baining The School of Computer Science and Engineering The Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China Microsoft Research Asia China

Recent works on language-guided image manipulation have shown great power of language in providing rich semantics, especially for face images. However, the other natural information, motions, in language is less explored. In this paper, we leverage the motion information and study a novel task, language-guided face animation, that aims to animate a static face image with the help of languages. To better utilize both semantics and motions from languages, we propose a simple yet effective framework. Specifically, we propose a recurrent motion generator to extract a series of semantic and motion information from the language and feed it along with visual information to a pre-trained StyleGAN to generate high-quality frames. To optimize the proposed framework, three carefully designed loss functions are proposed including a regularization loss to keep the face identity, a path length regularization loss to ensure motion smoothness, and a contrastive loss to enable video synthesis with various language guidance in one single model. Extensive experiments with both qualitative and quantitative evaluations on diverse domains (e.g., human face, anime face, and dog face) demonstrate the superiority of our model in generating high-quality and realistic videos from one still image with the guidance of language. Code will be available at https://***/TiankaiHang/*** Copyright © 2022, The Authors. All rights reserved.

关键词： Animation

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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An Efficient and Accurate Encrypted Image Retrieval Scheme via Ball Tree

An Efficient and Accurate Encrypted Image Retrieval Scheme v...

引用

International Conference on Big Data Computing and Communications (BIGCOM)

作者： Xianxian Li Jie Lei Zhenkui Shi Feng Yu College of computer science Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network Information Integration Southeast University Nanjing China

With the rise and development of cloud computing, more and more companies try to outsource computing and storage to cloud in order to save storage and computing cost. Due to the rich information contained in images, the explosion of images is booming the image outsourcing. However, images may contain a lot of sensitive information and cloud servers are always not trusted. Directly outsourcing may lead to data breaches and incur privacy and security concerns. This has partly led to renewed interest in privacy-preserving encrypted image retrieval. However, there are still many challenges, such as low search accuracy and inefficiency due to the hundreds of high dimensional features extracted from a single image and the large scale of images. To address these challenges, in this paper, we propose an efficient, scalable and privacy-preserving image retrieval scheme via ball tree. First, the pre-trained Convolutional Neural network (CNN) model is employed to extract image feature vectors to improve search accuracy. Next, an encrypted ball tree is constructed by using Learning With Errors(LWE)-based secure k-Nearest Neighbor (kNN) algorithm. Finally, we conduct comprehensive experiments on real-world datasets and give a brief security analysis. The results show that our scheme is practical in terms of security, accuracy, and efficiency.

关键词： Cloud computing Costs Image retrieval Feature extraction Search problems Explosions Outsourcing

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MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures

arXiv

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arXiv 2023年

作者： Wang, Zhuoyuan Mi, Jiacong Lu, Shan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd. Jiangsu Nanjing211161 China

The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representation of drug molecules emerges as a pivotal component in this pursuit. Contemporary leading-edge research predominantly resorts to self-supervised learning (SSL) techniques to extract meaningful structural representations from large-scale, unlabeled molecular data, subsequently fine-tuning these representations for an array of downstream tasks. However, an inherent shortcoming of these studies lies in their singular reliance on one modality of molecular information, such as molecule image or SMILES representations, thus neglecting the potential complementarity of various molecular modalities. In response to this limitation, we propose MolIG, a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. MolIG model innovatively leverages the coherence and correlation between molecule graph and molecule image to execute self-supervised tasks, effectively amalgamating the strengths of both molecular representation forms. This holistic approach allows for the capture of pivotal molecular structural characteristics and high-level semantic information. Upon completion of pre-training, Graph Neural network (GNN) Encoder is used for the prediction of downstream tasks. In comparison to advanced baseline models, MolIG exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups such as MoleculeNet Benchmark Group and ADMET Benchmark Group. © 2023, CC BY.

关键词： Molecules

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Brainnetformer: Decoding Brain Cognitive States with Spatial-Temporal Cross Attention

Brainnetformer: Decoding Brain Cognitive States with Spatial...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Leheng Sheng Wenhan Wang Zhiyi Shi Jichao Zhan Youyong Kong Chien-Shiung Wu College Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model.

关键词： Training Convolution Fuses Magnetic resonance imaging Predictive models Brain modeling Behavioral sciences

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Modeling and application of service relationships 3

Modeling and application of service relationships

引用

3rd IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2021

作者： Shaoxi, Liu Jun, Shen Jie, Ren Ying, Huang Southeast University Key Laboratory of Computer Network & Information Integration of Ministry of Education Nanjing China AVIC Chengdu Aircraft Design and Research Institute Chengdu China Nanjing Origin Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9781665425186

In some new network architectures, in order to improve the scalability of services, scholars in recent years have been trying their best to explore the strategy of combining several small-grained services into large-grained services that can respond to user needs. In response to this problem, this paper studies and analyzes the relationship between services, and proposes a service relationship model based on fragmented logic. The model defines the service relationship structure and its related relationship operations. Then the model is applied in the SDEN network to complete the system requirements of service dynamic combination, thereby verifying that the model can improve the flexibility and scalability of the actual application system. © 2021 IEEE

关键词： Scalability

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Accelerating Mega-Scale Satellite network Simulation in NS-3 via MPI-based Parallelization

Accelerating Mega-Scale Satellite Network Simulation in NS-3...

引用

IEEE International Conference on Communications (ICC)

作者： Haibin Song Tian Pan Guohao Ruan Yan Zheng Ying Wan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China China Mobile (Suzhou) Software Technology Co. Ltd Suzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Due to the high costs of low Earth orbit (LEO) satellite manufacturing and launch, as well as the complexity of in-orbit network protocol debugging, simulating and verifying satellite network protocols on the ground before satellite launch holds significant importance. Compared to the expensive emulation with one-to-one replication, simulation (e.g., using ns-3) can achieve discrete event processing at a relatively lower cost by extending the wall clock time. However, very few studies have used ns-3 for LEO satellite network simulation, facing challenges such as faithfully simulating the on/off state switching of inter-satellite links (ISLs) and achieving simulation performance scalability for high-density satellite constellations. In this work, we propose a system to accelerate mega-scale LEO satellite network simulation in ns-3 via MPI-based parallelization. Specifically, we simulate ISLs based on ns-3's P2P channels/P2P remote channels and achieve runtime link connection/disconnection by implementing stateful traffic dropping inside the network interface. Then, we conduct concurrent simulation with ns-3's parallel and distributed simulation capability and partition the satellite constellation into multiple simulation processes through a hierarchical clustering algorithm and automated scripts, considering satellite locality and inter-process workload balance. Our evaluation shows significant speed improvements via parallelization, e.g., a 373% speedup with 12 processes for LEO-192, and a 156% speedup with 3 processes for LEO-3072.

关键词： Satellite constellations Satellites Runtime Protocols Costs Low earth orbit satellites Clustering algorithms

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Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

arXiv

引用

arXiv 2022年

作者： Shi, Xinyu Wei, Dong Zhang, Yu Lu, Donghuan Ning, Munan Chen, Jiashun Ma, Kai Zheng, Yefeng School of Computer Science & Engineering Key Lab of Computer Network & Information Integration Ministry of Education Southeast Univ. Nanjing China Tencent Jarvis Lab Shenzhen China

Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images of the target class. A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images. However, most existing approaches either compressed the support information into a few class-wise prototypes, or used partial support information (e.g., only foreground) at the pixel level, causing non-negligible information loss. In this paper, we propose Dense pixel-wise Cross-query-and-support Attention weighted Mask Aggregation (DCAMA), where both foreground and background support information are fully exploited via multi-level pixel-wise correlations between paired query and support features. Implemented with the scaled dot-product attention in the Transformer architecture, DCAMA treats every query pixel as a token, computes its similarities with all support pixels, and predicts its segmentation label as an additive aggregation of all the support pixels’ labels—weighted by the similarities. Based on the unique formulation of DCAMA, we further propose efficient and effective one-pass inference for n-shot segmentation, where pixels of all support images are collected for the mask aggregation at once. Experiments show that our DCAMA significantly advances the state of the art on standard FSS benchmarks of PASCAL-5i, COCO-20i, and FSS-1000, e.g., with 3.1%, 9.7%, and 3.6% absolute improvements in 1-shot mIoU over previous best records. Ablative studies also verify the design DCAMA. © 2022, CC BY-NC-ND.

关键词： Pixels

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