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IEEE Conference on Decision and Control

作者： Ting Bai Alexander Johansson Karl Henrik Johansson Jonas Mårtensson Integrated Transport Research Lab and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665436601

This paper considers the problem of hub-based platoon coordination for a large-scale transport system, where trucks have individual utility functions to optimize. An event-triggered distributed model predictive control method is pro-posed to solve the optimal scheduling of waiting times at hubs for individual trucks. In this distributed framework, trucks are allowed to decide their waiting times independently and only limited information is shared between trucks. Both the predicted reward gained from platooning and the predicted cost for waiting at hubs are included in each truck’s utility function. The performance of the coordination method is demonstrated in a simulation with one hundred trucks over the Swedish road network.

关键词： Costs Roads Conferences Optimal scheduling Predictive control

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Stochastic Exceptional Points for Noise-Assisted Sensing

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Physical Review Letters 2023年第22期130卷 227201-227201页

作者： Zhipeng Li Chenhui Li Ze Xiong Guoqiang Xu Yongtai Raymond Wang Xi Tian Xin Yang Zhu Liu Qihang Zeng Rongzhou Lin Ying Li Jason Kai Wei Lee John S. Ho Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Wireless and Smart Bioelectronics Lab School of Biomedical Engineering ShanghaiTech University Shanghai 201210 China Department of Physiology Yong Loo Lin School of Medicine National University of Singapore Singapore 117593 Singapore School of Physics and Electronics Hunan Normal University Changsha Hunan 410081 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China Heat Resilience and Performance Centre Yong Loo Lin School of Medicine National University of Singapore Singapore 119228 Singapore Human Potential Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore 119228 Singapore

Noise is a fundamental challenge for sensors deployed in daily environments for ambient sensing, health monitoring, and wireless networking. Current strategies for noise mitigation rely primarily on reducing or removing noise. Here, we introduce stochastic exceptional points and show the utility to reverse the detrimental effect of noise. The stochastic process theory illustrates that the stochastic exceptional points manifest as fluctuating sensory thresholds that give rise to stochastic resonance, a counterintuitive phenomenon in which the added noise increases the system’s ability to detect weak signals. Demonstrations using a wearable wireless sensor show that the stochastic exceptional points lead to more accurate tracking of a person’s vital signs during exercise. Our results may lead to a distinct class of sensors that overcome and are enhanced by ambient noise for applications ranging from healthcare to the internet of things.

关键词： Noise Non-Hermitean systems PT-symmetry Noise measurements

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COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTIMATION 9

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTI...

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9th International Conference on Learning Representations, ICLR 2021

作者： Silva, Arlei Kocayusufoglu, Furkan Jafarpour, Saber Bullo, Francesco Swami, Ananthram Singh, Ambuj Computer Science Department UC Santa BarbaraCA93106-5110 United States Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States U.S. Army Research Lab AdelphiMD20783 United States

The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep learning

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Decoding the Obfuscated: Advanced NER Techniques on Commercial Sex Advertisement Data

Decoding the Obfuscated: Advanced NER Techniques on Commerci...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Alejandro Rodriguez Perez Pablo Rivas Javier Turek Korn Sooksatra Ernesto Quevedo Gisela Bichler Tomas Cerny Laurie Giddens Stacie Petter Dept. of Computer Science School of Eng. & Computer Science Baylor University Human-Robot Collaboration Lab Intelligent Systems Research Intel Labs School of Criminology & Criminal Justice California State University San Bernardino Dept. of Systems and Industrial Eng. College of Engineering The University of Arizona Information Tech. & Decisions Sci. Dept. G. Brint Ryan College of Business University of North Texas School of Business Wake Forest University

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Detecting potential human trafficking activity within online Commercial Sex Advertisements (CSAs) presents unique challenges for Named Entity Recognition (NER) due to the complex and often obfuscated textual content. This paper thoroughly evaluates state-of-the-art language models and tokenization techniques, focusing on their efficacy in the NER task within the context of CSAs. Our results indicate that the Longformer model, equipped with byte-level BPE tokenization, outperforms other models regarding precision, recall, and the $F$ 1 score. The study also uncovers specific areas for improvement, offering avenues for future research. Our findings have significant implications for the automated analysis and monitoring of CSAs for suspected human trafficking activity, which contributes to developing more robust and transparent online ecosystems.

关键词： Adaptation models Biological system modeling Focusing Named entity recognition Tokenization Data models Web sites

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Dynamic Curriculum Learning for Low-Resource Neural Machine Translation 28

Dynamic Curriculum Learning for Low-Resource Neural Machine ...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Xu, Chen Hu, Bojie Jiang, Yufan Feng, Kai Wang, Zeyang Huang, Shen Ju, Qi Xiao, Tong Zhu, Jingbo NLP Lab. School of Computer Science and Engineering Northeastern University Shenyang China Tencent Minority-Mandarin Translation Smart Platform Product Department Tencent Inc. China NiuTrans Research Shenyang China

ISBN: (纸本)9781952148279

Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Curricula

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Are you (Google) home? Detecting users’ presence through traffic analysis of smart speakers 4

Are you (Google) home? Detecting users’ presence through tr...

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4th Italian Conference on Cyber Security, ITASEC 2020

作者： Caputo, D. Verderame, L. Merlo, A. Ranieri, A. Caviglione, L. Computer Security Lab Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy Institute for Applied Mathematics and Information Technologies National Research Council of Italy Italy

smart speakers and voice-based virtual assistants are core building blocks of modern smart homes. For instance, they are used to retrieve information, interact with other devices, and command a variety of Internet of Things (IoT) nodes. To this aim, smart speakers and voice-based assistants typically take advantage of cloud architectures: vocal commands of the user are sampled, sent through the Internet to be processed and transmitted back for local execution, e.g., to perform an automation task or activate an IoT device. Even if privacy and security is enforced by means of encryption, features of the traffic, such as the throughput, the size of protocol data units or the IP addresses, can leak important information about the habits of the users as well as the number and the type of IoT nodes deployed. In this perspective, the paper showcases risks of machine learning techniques to develop black-box models to automatically classify traffic and implement privacy leaking attacks. We prove that such traffic analysis allows to detect the presence of a person in a house equipped with a Google Home device, even if the same person does not interact with the smart device. Experimental results collected in a realistic scenario are presented and possible countermeasures are discussed. Copyright © 2020 for this paper by its authors.

关键词： Internet of things

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A Q-learning-based smart clustering routing method in flying Ad Hoc networks

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Journal of King Saud University - computer and Information Sciences 2024年第1期36卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Aurangzeb, Khursheed Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Darwesh, Aso Lee, Sang-Woong Fazlali, Mahmood Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Cybersecurity and Computing Systems Research Group School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire AL10 9AB United Kingdom

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly. © 2024 The Author(s)

关键词： Clustering Flying ad hoc networks (FANETs) Machine learning (ML) Reinforcement learning (RL) Unmanned aerial vehicles (UAVs)

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DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning

arXiv

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arXiv 2022年

作者： Chen, Zhi Ji, Jiabao Chen, Lu Liu, Yuncong Ma, Da Chen, Bei Wu, Mengyue Zhu, Su Dong, Xin Ge, Fujiang Miao, Qingliang Lou, Jian-Guang Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University State Key Lab of Media Convergence Production Technology and Systems Beijing China Microsoft Research Asia China AISpeech Co. Ltd. Suzhou China

Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks. To achieve this goal, a large-scale well-annotated dialogue dataset with rich task diversity (DialogZoo) is collected. We introduce a framework to unify all dialogue tasks and propose novel auxiliary self-supervised tasks to achieve stable training of DFM on the highly diverse large scale DialogZoo corpus. Experiments show that, compared with models of the same size, DFM can achieve state-of-the-art or competitive performance on very rich cross-domain downstream dialogue tasks. This demonstrates that DFM largely extends the ability of unified dialogue pre-trained model. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Scattering-free plasmonic Brewster effect via metasurfaces

arXiv

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arXiv 2024年

作者： Zhang, Xinyan Cui, Xingshuo Cai, Tong Cai, Weiqi Low, Tony Chen, Hongsheng Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Air and Missile Defense College Air Force Engineering University Xi’an710051 China AVIC Research Institute for Special Structures of Aeronautical Composites Jinan250023 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China

The Brewster effect, dating back to the pioneering work of Sir David Brewster in 1815, offers a crucial route to achieve 100% energy conversion between the incident and transmitted propagating waves at an optical interface and is of fundamental importance to many practical applications, such as polarization filtering, beam steering, and optical broadband angular selectivity. However, whether the Brewster effect of surface waves can be implemented without the involvement of negative-permittivity or negative-permeability materials remains elusive. This is due to the formidable challenge to fully suppress both the parasitic scattering into propagating waves and the reflection into surface waves under the incidence of surface waves. Here, we reveal a feasible route to achieve scattering-free plasmonic Brewster effect via isotropic metasurfaces, along with the usage of positive-permittivity and positive-permeability metamaterials with both anisotropic and magnetic responses. In essence, the anisotropic response of metamaterials is judiciously designed to fully suppress the parasitic scattering into propagating waves, while the magnetic response of metamaterials facilitates the full suppression of the reflection into surface waves supported by metasurfaces. Moreover, we find that this plasmonic Brewster effect via metasurfaces can be further engineered to occur for arbitrary incident angles, giving rise to the exotic phenomenon of all-angle scattering-free plasmonic Brewster effect. © 2024, CC BY.

关键词： Surface waves

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