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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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Health-Care Monitoring of Patient using CNN based Model in Internet of Things 1

Health-Care Monitoring of Patient using CNN based Model in I...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Parameshachari, B.D. Hemalatha, K.L. Rahebi, Javad Baker, Mohammed Rashad Upendra Roy, B.P. Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Istanbul Topkapi University Department of Software Engineering Turkey University of Kirkuk College of Computer Science and Information Technology Department of Software Kirkuk Iraq Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350323795

There is rising public concern about exposure to radiofrequency (RF) electromagnetic fields (EMF) as more and more wireless communications become concentrated in everyday living surroundings. There are a number of obstacles, primarily originating from infrastructure expenses, but recent technology breakthroughs are shifting attention to Internet of Things (IoT) devices that enable automatic and continuous real-time EMF monitoring. The Internet of Things (IoT) has made the world a better place by expanding the capabilities of telemedicine and allowing for more precise remote monitoring of patients. Improved healthcare facilities for both patients and doctors/hospitals are only one way that IoT is making a significant impact in the medical field. Five main parts make up the proposed system: patient data gathering, primary report creation, hospital care, pharmacy care, and diagnostics. This study presents an Internet of Things (IoT)-based healthcare scheme accomplished of real-time nursing of energetic signs and environmental factors for discrete patients. Five sensors, including a heart-rate monitor, a body temperature monitor, a room temperature monitor, a carbon monoxide monitor, and a carbon dioxide monitor, are utilised to collect data from the hospital setting. Medical personnel receive patient updates through a portal and use this data for further diagnosis and care. The success of the system demonstrates that the built prototype is ideal for healthcare monitoring. This study has investigated unique approaches to the automatic construction of convolutional neural network (CNN) topologies, and it has also developed a new method for diagnosis. The results of the trials prove that the recommended algorithm achieves improved than the state-of-the-art techniques. © 2023 IEEE.

关键词： Diagnosis

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Privacy-preserving and efficient user matching based on attribute encryption in mobile social networks

Privacy-preserving and efficient user matching based on attr...

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作者： Wu, Lei Zhao, Shengnan Wei, Xia Meng, Lingzhen School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute-based encryption (CP-ABE), and the match server can perform friend recommendation by completing bi-directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead. © 2021 John Wiley & Sons, Ltd.

关键词： Cost reduction

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Fully-Scalable Massively Parallel Algorithm for k-center with Outliers 39

Fully-Scalable Massively Parallel Algorithm for k-center wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Di Feng, Qilong Huang, Junyu Xu, Jinhui Huang, Ziyun Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York BuffaloNY United States Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

ISBN: (纸本)157735897X

In this paper, we consider the k-center problem with outliers (the (k, z)-center problem) in the context of Massively Parallel Computation (MPC). Existing MPC algorithms for the (k, z)-center problem typically require Ω(k) local space per machine. While this may be feasible when k is small, these algorithms become impractical for large k, where each machine may lack sufficient space for computation. This motivates the study of fully-scalable algorithms with sublinear local space. We propose the first fully-scalable MPC algorithm for the (k, z)-center problem. The main challenge is to design an MPC algorithm that operates with sublinear local space for finding the inliers close to the optimal clustering centers, and ensuring the approximation loss remains bounded. To address this issue, we propose an iterative sampling-based algorithm with sublinear local space in the data size. A key component of our approach is an outliers-removal algorithm that adjusts the sample size in each iteration to select inliers as clustering centers. However, the number of discarded inliers increases with the iteration of the outliers-removal algorithm, making it difficult to bound. To address this, we propose a self-adaptive method that can automatically adjust sample size to account for different data distributions on each machine, ensuring a lower bound on the sampling success probability. With these techniques, we present an O(log∗ n)approximation MPC algorithm for the (k, z)-center problem in constant-dimensional Euclidean space. The algorithm discards at most (1 + ϵ)z outliers, completing in O(log log n) computation rounds while using Θ(nδ) local space per machine. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Clustering algorithms

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Design and Implementation of a Following Vehicle Based on ROS and UWB 7

Design and Implementation of a Following Vehicle Based on RO...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Yu, Yun-Shuai Chen, Chin-Wei National Formosa University Department of Computer Science and Information Engineering Yunlin Taiwan Software Research & Development Center New Taipei City Taiwan

ISBN: (纸本)9798350302882

Autonomous vehicle technology has gained significant attention in various industries recently. This paper presents the design and implementation of a following vehicle that utilizes the Robot Operating System (ROS) and Ultra-Wideband (UWB) RF transceiver modules to achieve precise target localization, map the surrounding environment, and navigate accordingly. A mobile application has also been developed to track the following vehicle's position. © 2023 IEEE.

关键词： Radio transceivers

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

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computers, Materials & Continua 2023年第2期74卷 3727-3741页

作者： Md Zahid Hasan Ontor Md Mamun Ali Kawsar Ahmed Francis M.Bui Fahad Ahmed Al-Zahrani S.M.Hasan Mahmud Sami Azam Department of Software Engineering(SWE) Daffodil International University(DIU)SukrabadDhaka1207Bangladesh Group of Biophotomatiχ Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonS7N5A9SKCanada Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Department of Computer Science American International University-Bangladesh(AIUB)KuratoliDhaka1229Bangladesh College of Engineering IT and EnvironmentCharles Darwin UniversityCasuarina0909NTAustralia

Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing *** this many women have ***,it is curable if it can be diagnosed and detected at an early stage and taken proper *** the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early *** solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell *** system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning *** build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and *** the YOLOv5 models were applied to the labeled dataset to train the *** versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early *** of the model’s variations performed *** model can effectively detect cervical cancerous cell,according to the findings of the *** the medical field,our study will be quite *** can be a good option for radiologists and help them make the best selections possible.

关键词： Cervical cancer pap-smear deep learning cancerous cell YOLOv5 model

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Composing Like an Ancient Chinese Poet:Learn to Generate Rhythmic Chinese Poetry

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Journal of computer science & Technology 2023年第6期38卷 1272-1287页

作者：何明陈艳赵洪科刘淇吴乐崔羽曾贵华刘贵全 Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China AI Laboratory Lenovo ResearchBeijing 100193China Baidu Incorporated Beijing 100193China College of Management and Economics Tianjin UniversityTianjin 300072China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Risk Control Department OPayBeijing 100010China

Automatic generation of Chinese classical poetry is still a challenging problem in artificial ***-cently,Encoder-Decoder models have provided a few viable methods for poetry ***,by reviewing the pri-or methods,two major issues still need to be settled:1)most of them are one-stage generation methods without further polishing;2)they rarely take into consideration the restrictions of poetry,such as tone and ***,some an-cient Chinese poets tended first to write a coarse poem underlying aesthetics and then deliberated its semantics;while oth-ers first create a semantic poem and then refine its *** this basis,in order to better imitate the human creation procedure of poems,we propose a two-stage method(i.e.,restricted polishing generation method)of which each stage fo-cuses on the different aspects of poems(i.e.,semantics and aesthetics),which can produce a higher quality of generated *** this way,the two-stage method develops into two symmetrical generation methods,the aesthetics-to-semantics method and the semantics-to-aesthetics *** particular,we design a sampling method and a gate to formulate the tone and rhyme restrictions,which can further improve the rhythm of the generated *** results demon-strate the superiority of our proposed two-stage method in both automatic evaluation metrics and human evaluation met-rics compared with baselines,especially in yielding consistent improvements in tone and rhyme.

关键词： aesthetics poetry generation polishing semantics two-stage method

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Detecting and Corrupting Convolution-based Unlearnable Examples 39

Detecting and Corrupting Convolution-based Unlearnable Examp...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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