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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qu, Jing Qu, Ying Bai, Xueliang Xu, Lanxin Wang, Han Liu, Guixia Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China North China University of Science and Technology College of Science Tangshan China Institute of Computational Biology Northeast Normal University School of Information Science and Technology Changchun China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer's disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression. © 2024 IEEE.

关键词： Neuroimaging

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Energy Consumption-Driven UAV Dock Deployment Planning for Power Inspection

Energy Consumption-Driven UAV Dock Deployment Planning for P...

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Fu, Jing Shao, Guiwei Wang, Zhiye Wu, Shaolong Cai, Huanqing Wu, Huaxi Chen, Chi Yang, Bisheng China Electric Power Research Institute Co. Ltd Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application Ministry of Education of China China Institute of Geo-spatial Intelligence Wuhan University Wuhan China

As an important energy in modern society, the stable operation of electricity is the guarantee of social and economic operation, and the loss caused by the damage of power facilities is immeasurable, so the regular monitoring of power facilities is indispensable. UAVs, with flexible flight mode, have played an important role in power inspection. With the development of UAV docks, UAV inspection has entered a new stage. However, due to the fixed location of the docks and limited coverage, how to achieve the optimal deployment of the docks with the lowest cost and full coverage of power facilities through reasonable deployment is one of the important issues that need to be solved at this stage of dock construction. To solve this problem, this paper proposes an energy-driven adaptive optimization method for dock location by combining geospatial analysis with UAV performance index. The experiment results show that our method can achieve an efficient, reasonable and feasible optimal location for UAV dock deployment. © Author(s) 2024.

关键词： Site selection

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Regulation of morphology and visible light-driven photocatalysis of WO_(3)nanostructures by changing pH

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Rare Metals 2021年第7期40卷 1738-1745页

作者： You-Shu Fan Xiao-Li Xi Yang-Si Liu Zuo-Ren Nie Lin-Yan Zhao Qing-Hua Zhang College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic performance of the samples was characterized via X-ray diffraction(XRD),scanning electron microscopy,energy dispersive X-ray spectroscopy,and Raman ***-visible(UV-Vis)spectra were used to evaluate the absorbance and the photocatalytic performance of methylene ***(PL),electrochemical impedance spectroscopy,photocurrent response and Brunauer-Emmett-Teller(BET)were used to study the optical properties,electrical performance,and specific surface area of the WO_(3)-nanostructures,*** results indicate that the WO_(3) nanorods prepared at pH=1.0 exhibit the highest photocatalytic performance(87.4%in 1 h),whereas the WO_(3) nanoblocks prepared at p H=3.0 show the *** photocatalytic performance of the one dimensional(1 D)-nanorods can be attributed to their high specific surface area and charge transfer *** h-WO_(3) nanostructures were synthesized via a simple method and without a capping *** show an excellent photocatalytic performance,which is promising for their application in environment purification.

关键词： h-WO_(3) Morphology pH Photocatalysis Charge transfer Optoelectrical properties

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Chinese Lexical Simplification

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IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 1819-1828页

作者： Qiang, Jipeng Lu, Xinyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China

Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for the Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. To acquire a more thorough comparison, we present five different types of methods as baselines to generate substitute candidates for the complex word that includes synonym-based approach, word embedding-based approach, BERT-based approach, sememe-based approach, and a hybrid approach. Finally, we design the experimental evaluation of these baselines and discuss their advantages and disadvantages. To our best knowledge, this is the first study for CLS task. © 2014 IEEE.

关键词： BERT Lexical simplification pretrained language model unsupervised

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology 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 China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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Large Scale Model Enabled Semantic Communications Based on Robust knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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A Differential Privacy Protection Protocol Based on Location Entropy

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Tsinghua Science and Technology 2023年第3期28卷 452-463页

作者： Ping Guo Baopeng Ye Yuling Chen Tao Li Yixian Yang Xiaobin Qian Xiaomei Yu State Key Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Information Technology Innovation Service Center of Guizhou Province Guiyang 550025China School of Cyberspace Security Beijing University of Posts and TelecommnuicationsBeijing 100000China Guizhou CoVision Science&Technology Co. Ltd.Guiyang 550025China School of Information Science and Engineering Shandong Normal UniversityJinan 250000China

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and *** an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and *** solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location ***,we design an algorithm of the best-assisted user selection for constructing anonymity ***,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of ***,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge ***,our protocol improves data ***,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

关键词： Location-Based Services(LBS) smart contract location entropy differential privacy privacy protection

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Research on Whole-Link Risk Situational Awareness Index System and Dynamic Risk Pool Supervision 11

Research on Whole-Link Risk Situational Awareness Index Syst...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Sun, Jingchun Deng, Fei Du, Boya School of Management Xi'An Jiaotong University China Key Laboratory of the Ministry of Education for Process Control and Efficiency Engineering China College of Information Engineering Kunming University China Key Laboratory of Data Governance and Intelligent Decision Universities of Yunnan China

ISBN: (纸本)9781450398039

With the development of society, network security becomes more and more important. It is inevitable to establish the risk index system to effectively monitor the security of network. In the previous study, due to absence of risk indicators, the risk supervising system is not perfect. However, there are few researches on the whole-link risk index system, and the determination of risk indicators lacks basis. Therefore, this paper firstly constructs a new risk index system of situational awareness, which can avoid the omission of risk indicators. Then the sliding window is used to observe this system and the optimal window width is found to determine the dynamic risk pool. Finally, the indicators of dynamic risk pool are used to dynamically monitor the whole link risk index system. The whole technical framework not only proves advantages of the mature risk index system of situational awareness, but also overcomes the disadvantage that the risk index of the whole link cannot be determined, and the dynamic risk pool has the sharing characteristics in the whole process. © 2022 ACM.

关键词： Network security

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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Theoretical data Science: bridging the gap between domain-general and domain-specific studies

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data Science and Informetrics 2021年第1期1卷 1-28页

作者： Chaolemen Borjigin Chen Zhang Zhizong Sun Ni Yi Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina School of Information Resource Management Renmin University of ChinaBeijingChina

The entering into big data era gives rise to a novel discipline called data *** Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent *** domain-dependent studies and domain-independent ones are evolving into Domain-general data Science and Domain-specific data ***-general data Science emphasizes data Science in a general sense,involving concepts,theories,methods,technologies,and ***-specific data Science is a variant of Domain-general data Science and varies from one domain to *** most popular Domain-specific data Science includes data journalism,Industrial data Science,Business data Science,Health data Science,Biological data Science,Social data Science,and Agile data *** difference between Domain-general data Science and Domain-specific data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered *** a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ***,DSDS and DGDS possess complementary *** data Science(TDS)is a new branch of data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data *** will bridge the gap between DGDS and *** contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of data Science *** main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual ***,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data *** studies of TDS should be focused

关键词： data Science Big data Theoretical data Science Domain-general data Science Domain-specific data Science

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