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2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2023

作者： Wu, Qi Chao, Wenhan Zhou, Xian Luo, Zhunchen School of Computer Science and Engineering Beihang University China Information Research Center of Military Science PLA Academy of Military Science China

This paper introduces a novel task of detecting turning points in the engineering process of large-scale projects, wherein the turning points signify significant transitions occurring between phases. Given the complexities involving diverse critical events and limited comprehension in individual news reports, we approach the problem by treating the sequence of related news streams as a window with multiple instances. To capture the evolution of changes effectively, we adopt a deep Multiple Instance Learning (MIL) framework and employ the multiple instance ranking loss to discern the transition patterns exhibited in the turning point window. To facilitate comprehensive evaluation of the task, we curate a dataset comprising 80 large-scale projects. Extensive experiments consistently demonstrate the effectiveness of our proposed approach on the constructed dataset compared to baseline methods. We deployed the proposed model1 and provided a demonstration video2 to illustrate its functionality. The code and dataset are available on GitHub. © 2023 Association for Computational Linguistics.

关键词： Large datasets

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Dual Encoder Cross-Shape Transformer Network for Medical Image Segmentation in Internet of Medical Things for Consumer Health

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 538-549页

作者： Zhao, Yawu Wang, Shudong Zhang, Yulin Ren, Yande Zhang, Yuanyuan Pang, Shanchen the School of Computer Science and Technology China University of Petroleum Shandong Qingdao China the College of Mathematics and System Science Shandong University of Science and Technology Shandong Qingdao China the Department of Medical Imaging Center the Affiliated Hospital of Qingdao University Shandong Qingdao China the School of Information and Control Engineering Qingdao University of Technology Shandong Qingdao China

In emerging consumer healthcare, high-performance and robust medical image segmentation methods are essential for personalized diagnosis and treatment. Thus, early screening of aneurysms reduces the risk of aneurysm rupture and provides therapeutic and preventive measures. However, uncontrollable factors such as uncertainty in the size and location shape of tumors in medical images a significant challenge to medical image segmentation. These factors make extracting high-quality features from aneurysm images difficult, resulting in poor segmentation. Then, we designed a dual encoder cross-shape transform network (DECSTNet) to capture aneurysm feature information. The dual encoder structure can extract aneurysm feature information at different scales, the adaptive dynamic feature fusion module can fuse features at different scales between the encoders, and the cross-shape window transform layer can compute the width and height of the image in parallel for local self-attention, which enhances the interactive capability of the telematic information while realizing the complementarity of the local information. Through extensive experiments, we demonstrate the excellent segmentation performance of DECSTNet on the private and three public datasets. Noteworthy our method has significant segmentation performance and fewer parameters, making it well-suited to be deployed on IoMT diagnostic platforms for medical image segmentation to promote healthy patient consumption. © 2025 IEEE.

关键词： Semantic Segmentation

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Optical sorting:past,present and future

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光：科学与应用（英文版） 2025年第5期14卷 1188-1234页

作者： Meng Yang Yuzhi Shi Qinghua Song Zeyong Wei Xiong Dun Zhiming Wang Zhanshan Wang Cheng-Wei Qiu Hui Zhang Xinbin Cheng Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai 200092China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China Shanghai Frontiers Science Center of Digital Optics Shanghai 200092China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

Optical sorting combines optical tweezers with diverse techniques,including optical spectrum,artificial intelligence(AI)and immunoassay,to endow unprecedented capabilities in particle *** comparison to other methods such as microfluidics,acoustics and electrophoresis,optical sorting offers appreciable advantages in nanoscale precision,high resolution,non-invasiveness,and is becoming increasingly indispensable in fields of biophysics,chemistry,and materials *** review aims to offer a comprehensive overview of the history,development,and perspectives of various optical sorting techniques,categorised as passive and active sorting *** begin,we elucidate the fundamental physics and attributes of both conventional and exotic optical *** then explore sorting capabilities of active optical sorting,which fuses optical tweezers with a diversity of techniques,including Raman spectroscopy and machine ***,we reveal the essential roles played by deterministic light fields,configured with lens systems or metasurfaces,in the passive sorting of particles based on their varying sizes and shapes,sorting resolutions and *** conclude with our vision of the most promising and futuristic directions,including Al-facilitated ultrafast and bio-morphology-selective *** can be envisioned that optical sorting will inevitably become a revolutionary tool in scientific research and practical biomedical applications.

关键词： present future past optical sorting

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Angle robust transmitted plasmonic colors with different surroundings utilizing localized surface plasmon resonance

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Chinese Physics B 2023年第7期32卷 226-233页

作者：高旭峰王琦张世杰洪瑞金张大伟 Shanghai Key Laboratory of Modern Optic Systems Engineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optical SystemsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Color filters in different surroundings inherently suffer from angular sensitivity,which hinders their practical ***,we present an angle-insensitive plasmonic filter that can produce different color responses to different surrounding *** color filters are based on a two-dimensional periodically and randomly distributed silver nanodisk array on a silica *** proposed plasmonic color filters not only produce bright colors by altering the diameter of the Ag nanodisk,but also achieve continuous color palettes by changing the surrounding *** to the weak coupling between the metallic nanodisks,the plasmonic color filters can enable good incident angle-insensitive properties(up to 30°).The strategy presented here could exhibit robust and promising applicability in anti-counterfeiting and imaging technologies.

关键词： plasmonic color filter color sensing high angular tolerance

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering 7

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Adaptive IoT Decision Making in Uncertain Environments 7

Adaptive IoT Decision Making in Uncertain Environments

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7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023

作者： Wang, Yan Zheng, Liwei He, Jingjing Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350316575

The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, making it difficult to predict and control their behavior. In this paper, we propose an adaptive decision making approach for IoT systems in uncertain, dynamic environments. We present a framework with perception, decision and execution layers to handle uncertainty in IoT systems. The perception layer senses the environment and system state. The decision layer employs an optimized deep Q-network algorithm (Ad-DQN) specifically designed to handle uncertain environments, enabling it to make informed decisions based on learned experiences. The execution layer implements the actions. We demonstrate the framework on an intelligent air conditioning system as a case study of an IoT system operating under uncertainty. The Ad-DQN based decision layer adapts the air conditioning control policy to maximize comfort while minimizing energy usage. Experiments show our method outperforms traditional DQN method in uncertain environments. © 2023 IEEE.

关键词： Reinforcement learning

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An Intelligent Interface of Video Retrieval Based on Summary CARD of Query Words

An Intelligent Interface of Video Retrieval Based on Summary...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Wu, Runxun Lin, Shujin Zhao, Baoquan Zhou, Fan School of Computer Science and Engineering Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou China School of Communication and Design Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou China

ISBN: (纸本)9798331509873

The large amount of video resources on the Internet brings huge challenges to users' retrieval. Therefore, this paper designs an algorithm to automatically generate video card summaries, which contain rich graphic and text materials and can effectively summarize video content. In order to facilitate the division of the hierarchical structure of the video, we also propose a model based on bidirectional transformer for video scene segmentation. Experiments on benchmark datasets show that our method achieves competitive performance. In order to visually display the algorithm, we further design a visual intelligent interface system for news or educational video retrieval results. © 2024 IEEE.

关键词： Video analysis

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Optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system and compressed sensing

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Chinese Physics B 2023年第11期32卷 426-445页

作者：都洋隆国强蒋东华柴秀丽韩俊鹤 Center for Physics of Low-Dimensional Materials Henan Joint International Research Laboratory of New Energy Materials and DevicesSchool of Physics and ElectronicsHenan UniversityKaifeng 475004China School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of ThingsHenan UniversityZhengzhou 450046China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China

Some existing image encryption schemes use simple low-dimensional chaotic systems, which makes the algorithms insecure and vulnerable to brute force attacks and cracking. Some algorithms have issues such as weak correlation with plaintext images, poor image reconstruction quality, and low efficiency in transmission and storage. To solve these issues,this paper proposes an optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system(4D MHS) and compressed sensing(CS). Firstly, this paper proposes a new 4D MHS, which has larger key space, richer dynamic behavior, and more complex hyperchaotic characteristics. The introduction of CS can reduce the image size and the transmission burden of hardware devices. The introduction of double random phase encoding(DRPE) enables this algorithm has the ability of parallel data processing and multi-dimensional coding space, and the hyperchaotic characteristics of 4D MHS make up for the nonlinear deficiency of DRPE. Secondly, a construction method of the deterministic chaotic measurement matrix(DCMM) is proposed. Using DCMM can not only save a lot of transmission bandwidth and storage space, but also ensure good quality of reconstructed images. Thirdly, the confusion method and diffusion method proposed are related to plaintext images, which require both four hyperchaotic sequences of 4D MHS and row and column keys based on plaintext images. The generation process of hyperchaotic sequences is closely related to the hash value of plaintext images. Therefore, this algorithm has high sensitivity to plaintext images. The experimental testing and comparative analysis results show that proposed algorithm has good security and effectiveness.

关键词： memristor hyperchaotic system compressed sensing fractional Fourier transform optical image encryption

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