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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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CTFN: Hierarchical learning for multimodal sentiment analysis using coupled-translation fusion network 59

CTFN: Hierarchical learning for multimodal sentiment analysi...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Tang, Jiajia Li, Kang Jin, Xuanyu Cichocki, Andrzej Zhao, Qibin Kong, Wanzeng Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province School of Computer Science and Technology Hangzhou Dianzi University China Skolkovo Institute of Science and Technology Moscow Russia Systems Research Institute Polish Academy of Science Warsaw Poland Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781954085527

Multimodal sentiment analysis is the challenging research area that attends to the fusion of multiple heterogeneous modalities. The main challenge is the occurrence of some missing modalities during the multimodal fusion procedure. However, the existing techniques require all modalities as input, thus are sensitive to missing modalities at predicting time. In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities. Specifically, the cyclic consistency constraint is presented to improve the translation performance, allowing us directly to discard decoder and only embraces encoder of Transformer. This could contribute to a much lighter model. Due to the couple learning, CTFN is able to conduct bi-direction cross-modality intercorrelation parallelly. Based on CTFN, a hierarchical architecture is further established to exploit multiple bi-direction translations, leading to double multimodal fusing embeddings compared with traditional translation methods. Moreover, the convolution block is utilized to further highlight explicit interactions among those translations. For evaluation, CTFN was verified on two multimodal benchmarks with extensive ablation studies. The experiments demonstrate that the proposed framework achieves state-of-the-art or often competitive performance. Additionally, CTFN still maintains robustness when considering missing modality. © 2021 Association for Computational Linguistics

关键词： Sentiment analysis

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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Soft Artificial Synapse Electronics

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Research 2024年第4期8卷 101-118页

作者： Md.Rayid Hasan Mojumder Seongchan Kim Cunjiang Yu Department of Electrical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Materials Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Materials Research Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA

Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow

关键词： dielectric utilizing gained

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Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications

Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Re...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Nikumani Choudhury Manik Gupta Moustafa M. Nasralla Satoshi Fujita Dept. of Computer Science and Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Prince Sultan University Riyadh Saudi Arabia Graduate School of Advanced Science and Engineering Hiroshima University Japan

With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF).

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Analysis and Detection of Fake News Using Machine Learning

Analysis and Detection of Fake News Using Machine Learning

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

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StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing

StressBal: Personalized Just-in-time Stress Intervention wit...

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable computers

作者： Yunjo Han Hyemin Lee Kobiljon E. Toshnazarov Youngtae Noh Uichin Lee Korea Advanced Institute of Science & Technology (KAIST) Korea Republic of Hanyang University Korea Republic of Electrical and Computer Engineering / Engineering school / Networked Systems Lab Inha University Korea Republic of KENTECH Korea Republic of School of Computing KAIST Korea Republic of

ISBN: (纸本)9781450394239

This paper proposes StressBal, a mobile system that offers personalized just-in-time stress intervention with mobile and wearable sensing. The system runs entirely on-device, detecting stress in real-time and providing respiratory exercise-based interventions. It also updates the stress detection algorithm continuously, based on user feedback. We envision an open mobile platform that helps researchers to design and evaluate automatic, seamless, and unobtrusive stress interventions during everyday activities.

关键词： Mobile and Wearable Computing Stress Intervention Just-in-time Intervention Stress Detection

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Using deep learning to predict matched signal-to-noise ratio of gravitational waves

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Physical Review D 2024年第4期109卷 043009-043009页

作者： CunLiang Ma Sen Wang Wei Wang Zhoujian Cao School of Information Engineering Jiangxi University of Science and Technology Ganzhou 341000 China School of Computer Science Fudan University Shanghai 201203 China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China

The existing matched filtering method for gravitational wave (GW) search relies on a template bank. The computational efficiency of this method scales with the sizes of the templates within the bank. Higher-order modes and eccentricity will play an important role when third-generation detectors operate in the future. In this case, traditional GW search methods will hit computational limits. To speed up the computational efficiency of GW searches, we propose the utilization of a deep learning (DL) model bank as a substitute for the template bank. This model bank predicts the latent templates embedded in the strain data. Combining an envelope extraction network and an astrophysical origin discrimination network, we realize a novel GW search framework. The framework can predict the GW signal’s matched filtering signal-to-noise ratio (SNR). Unlike the end-to-end DL-based GW search method, our statistical SNR holds stronger physical interpretability than the pscore metric. Moreover, the intermediate results generated by our approach, including the predicted template, offer valuable assistance in subsequent GW data processing tasks such as parameter estimation and source localization. Compared to the traditional matched filtering method, the proposed method can realize real-time analysis.

关键词： Classical black holes Experimental studies of gravity Gravitation Gravitational wave detection Gravitational waves

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Digital Signage System Using a Directional Speaker and Displaying a Target Image

Digital Signage System Using a Directional Speaker and Displ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Toshiya Watanabe Keito Ono Satoshi Ohtsuka Dept. of Information and Computer Engineering National Institute of Technology(KOSEN) Gunma College Maebashi Gunma Japan Advanced Production Systems Engineering Cource National Institute of Technology(KOSEN) Gunma College Maebashi Gunma Japan

ISBN: (纸本)9781665492331

Many recent digital signage systems use sensors to switch content based on surrounding conditions dynamically. However, many people are unaware of the displayed content. Therefore, we considered a design to improve this. In this study, we developed and evaluated a digital signage system with a directional speaker and the ability to present an image of the target. We designed and evaluated a digital signage system that uses a directional speaker to call out and present an image of the subject. Our proposed system showed that the subjects had increased the impression of the advertisements.

关键词： Target tracking Switches Sensor systems Sensors Consumer electronics

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