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Internet of Things Challenges and the Emerging Technology of TinyML 19

Internet of Things Challenges and the Emerging Technology of...

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Tsoukas, Vasileios Gkogkidis, Anargyros Kakarountas, Athanasios University of Thessaly Intelligent Systems Laboratory Department of Computer Science and Biomedical Informatics Lamia Greece

ISBN: (纸本)9798350346497

The merger of Internet of Things (IoT) and Machine Learning undoubtedly offers several benefits, but due to the nature of IoT devices and their underlying design, a number of difficulties and challenges exist. The most fundamental of these has to do with the security dangers posed by the transmission of voluminous amounts of data, as well as several networking obstacles, such as delay and bandwidth constraints. Moving all processing close to the network's edge and preventing the transmission of data to third parties, such as the cloud or other devices, is a potential solution. Embedded Machine Learning, and TinyML in particular, is a new technology that allows the implementation of Machine Learning on hardware with limited resources, such as Microcontrollers and FPGAs. In this paper, the fundamental issues of IoT devices are examined, the TinyML technology's advantages are outlined, and a summary of the most recent TinyML apps and systems is offered. © 2023 IEEE.

关键词： Microcontrollers

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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Incentives in Private Collaborative Machine Learning 37

Incentives in Private Collaborative Machine Learning

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Sim, Rachael Hwee Ling Zhang, Yehong Hoang, Trong Nghia Xu, Xinyi Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Peng Cheng Laboratory China School of Electrical Engineering and Computer Science Washington State University United States Dept. of Electrical Engineering and Computer Science MIT United States

ISBN: (纸本)9781713899921

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition's model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition's posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Machine learning

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Integrated quantum photonics in 3C-SiCOI platforms

Integrated quantum photonics in 3C-SiCOI platforms

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Smart Photonic and Optoelectronic Integrated Circuits 2025

作者： Poon, Andrew W. Zhang, Qianni Wang, Jiantao Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781510684881

3C-silicon carbide-on-insulator (3C-SiCOI) is an emerging photonic platform for integrated quantum photonics due to its moderate second- and third-order optical nonlinearities, wide bandgap energy, and compatibility with silicon complementary metal-oxide-semiconductor technologies. In this work, we report our progress in photon-pair generation and quantum state manipulation on the 3C-SiCOI platform. We demonstrate the generation of photon pairs through the spontaneous parametric down-conversion process using modal phase-matching in an elliptical microring resonator on this platform. We will present the design principle and recent progress of our experimental work. © 2025 SPIE.

关键词： Photonic integrated circuits

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Mapping Explicit and Implicit Discourse Relations between the RST-DT and the PDTB 3.0

Mapping Explicit and Implicit Discourse Relations between th...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Costa, Nelson Filipe Sheikh, Nadia Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University MontréalQC Canada

ISBN: (纸本)9789544520922

In this paper we propose a first empirical mapping between the RST-DT and the PDTB 3.0. We provide an original algorithm which allows the mapping of 6,510 (80.0%) explicit and implicit discourse relations between the overlapping articles of the RST-DT and PDTB 3.0 discourse annotated corpora. Results of the mapping show that while it is easier to align segments of implicit discourse relations, the mapping obtained between the aligned explicit discourse relations is more unambiguous. © 2023 Incoma Ltd. All rights reserved.

关键词： Mapping

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Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding 1

Detect misinformation of COVID-19 using deep learning: A com...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Khoudi, Asmaa Yahiaoui, Nessrine Rebahi, Feriel University m'Hamed Bougara Faculty of Sciences LIMOSE Laboratory Computer Science Department Boumerdes Algeria University m'Hamed Bougara Faculty of Sciences Computer Science Department Boumerdes Algeria

ISBN: (纸本)9781665472753

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set. © 2023 IEEE.

关键词： COVID-19

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Intelligent Innovation Dataset on Scientific Research Outcomes and Patents

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Journal of Social Computing 2025年第1期6卷 63-73页

作者： Wu, Xinran Zou, Hui Xing, Yidan Qu, Jingjing Guo, Weiwei Fu, Xiaoming School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Shanghai University Department of Library and Information Science Shanghai200444 China Big Data Technology Co. Ltd. Shanghai200082 China Shanghai Artificial Intelligence Laboratory Shanghai200082 China Fudan University Department of Management Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Institute of Computer Science University of Göttingen Göttingen37073 Germany

Various stakeholders, such as researchers, government agencies, businesses, and research laboratories require a large volume of reliable scientific research outcomes including research articles and patent data to support their work. These data are crucial for a variety of application, such as advancing scientific research, conducting business evaluations, and undertaking policy analysis. However, collecting such data is often a time-consuming and laborious task. Consequently, many users turn to using openly accessible data for their research. However, these existing open dataset releases typically suffer from lack of relationship between different data sources and a limited temporal coverage. To address this issue, we present a new open dataset, the Intelligent Innovation Dataset (IIDS), which comprises six interrelated datasets spanning nearly 120 years, encompassing paper information, paper citation relationships, patent details, patent legal statuses, and funding information. The extensive contextual and extensive temporal coverage of the IIDS dataset will provide researchers and practitioners and policy maker with comprehensive data support, enabling them to conduct indepth scientific research and comprehensive data analyses. © 2020 Tsinghua University Press.

关键词： Decentralized finance

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mmAlphabet: Air Writing Alphabet Recognition System Based on mmWave FMCW Radar and Convolutional Neural Network 27th

mmAlphabet: Air Writing Alphabet Recognition System Based on...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Chao-Wang Wang, Chien-Yao Wang, Jia-Ching Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Multimedia Technologies Laboratory Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9783031781032

An air writing alphabet recognition system based on the images of temporal spectrogram such as average range-time map, average Doppler-time map and average angle-time map derived from an mmWave FMCW radar is proposed. All 26 English lowercase letters written in midair right above the radar sensor can be recognized. Two valuable radar data sets are introduced in diverse environments such as meeting room, office cubicle and living room etc. The use case is the usage scenarios of laptop computers and mobile phones. In one data set, volunteers write freely in their own handwriting styles including different hand speeds and different stroke orders. In the other data set, volunteers are asked to write according to a prescribed sequence of strokes. Then, the gestures sensed by radar are processed into images of temporal spectrogram to represent the written letter. A convolutional neural network which achieves 98.6% test accuracy is exploited as the classifier to recognize the air written alphabet. In the Leave-One-Subject-Out (LOSO) cross validation, it achieves an average test accuracy of 87.74%. The effectiveness of the proposed alphabet recognition system is extensively verified on the two created data sets for different variants of temporal spectrograms. It can be used to implement natural, intelligent noncontact human machine interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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FedTG: Text-guided Federated Domain Generalization

FedTG: Text-guided Federated Domain Generalization

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Yiming He, Nan Sun, Lifeng Department of Computer Science and Technology Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education China

ISBN: (纸本)9798350368741

Federated Domain Generalization is aimed at learning a global model that generalizes well to new clients with domain shift in a privacy-preserving manner. Inspired by the powerful generation capability of Large language models (LLMs), we propose a novel method for this problem, named Text-guided Federated Domain Generalization (FedTG), which utilizes text generated by LLMs to guide the training of visual models. In the client, we align the image embeddings to the text embeddings, which provides strong generalization knowledge. In the server, we generate abundant textual descriptions about different classes, subsequently converting them into embeddings for fine-tuning the global classifier, which helps the global classifier not overfit to a particular domain. Moreover, we utilize the powerful image generation capabilities of LLMs to generate a cross-domain validation dataset to help select the most generalized model. We conduct experiments on several datasets, and the results prove that FedTG outperforms relevant baselines. © 2025 IEEE.

关键词： Data Heterogeneity Domain Shift Federated Domain Generalization Federated Learning

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Transformer-Based Question Answering Model for the Biomedical Domain 5

Transformer-Based Question Answering Model for the Biomedica...

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Haddouche, Ahcene Rabia, Ikram Aid, Aicha Bouira University Faculty of Sciences and Applied Sciences LIM Laboratory Computer Science Department Algeria

ISBN: (纸本)9798350381450

Motivation: Question Answering (QA) is a highly focused topic in the field of Natural Language Processing (NLP). Recent progress in neural network models and the availability of large datasets like SQuAD have played a significant role in improving performance in open domains. However, there remains a need to further effectively implement these systems in more specific domains, especially in the biomedical field, to help medical practitioners provide accurate solutions for inquiries related to medicine and healthcare, including specific subjects such as the COVID-19 disease. Fortunately, recent models, such as transformers, have opened up avenues and modern techniques for developing accurate ***: In this work, we aim to leverage transformer models and Transfer Learning to effectively train models in the biomedical domain. By taking a pre-Trained model for Question Answering tasks and further fine-Tuning it on specific domains, we enhance the system's performance in the biomedical domain. Our ultimate goal is to develop a QA model specifically tailored for COVID-19 ***: We have trained BERT and RoBERTa models on the COVID-QA dataset and achieved competitive results on COVID-19 QA. Our RoBERTa model achieved an Exact Match (EM)/F1 score of 0.38/0.64, respectively, on COVID-QA, indicating successful performance in COVID-19 QA. © 2023 IEEE.

关键词： COVID-19

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