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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Xinyu Zhao, Meng Shi, Fan Zhang, Meng'en He, Yu Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Laboratory of Computer Vision and System of Ministry of Education Tianjin300384 China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Beijing100190 China

ISBN: (纸本)9798350331820

With the success of multimodal pre-training models in the video-language field and various downstream tasks, previous multimodal models used 3DCNN networks as video feature extractors, which have limitations in interacting and fusing with text features. This paper proposes a multimodal pre-training model that utilizes a Video-Swin-Transformer-based network to encode both video and text data, to achieve better performance in video understanding. The model consists of four modules: video encoder, text encoder, interact encoder, and caption decoder to accomplish the task of ocean scene video captioning. A dataset of ocean scene videos, including various content types such as sea surfaces and shores, is also constructed. The training process is divided into two stages: pre-training and fine-tuning. Pre-training is performed on the Howto100m dataset to allow the model to learn video captions in natural scenes and complete video-language matching tasks. The fine-tuning stage is then performed on the ocean1000 dataset to better understand the events and content in ocean scene videos and generate captions that conform to ocean scene video descriptions. The model achieves satisfying results on both the public dataset YouCook2 and the proprietary dataset Ocean1000, demonstrating its ability in video-text information fusion and interaction. © 2023 IEEE.

关键词： Signal encoding

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Integrating GPS and GSM Technologies for Enhanced Women's Safety: A Fingerprint-Activated Device Approach

Integrating GPS and GSM Technologies for Enhanced Women's Sa...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Chaudhary, Varun Sharma, Pradeep Kumar Kumar, Pushpendra Varshney, Neeraj Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Ims Engineering College Ghaziabad India Hcl Technologies UP Noida India Scanno System Greater Noida India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

The creation of a safety system that is based on GPS and is developed exclusively for the health and safety of women via the incorporation of novel elements in order to meet the rising concerns about crimes committed against women. The gadget is capable of contacting authorities, conveying the user's position via SMS, and generating a loud noise to attract attention. It may be triggered in anticipation of potential danger and can be activated in the event that danger is imminent. In order to guarantee that only authorized users are able to activate the device, the system makes use of fingerprint recognition in a very unique way. Using technologies such as global positioning system (GPS), global system for mobile communications (GSM), and microcontrollers, this study makes a contribution to the continuing efforts to improve the safety of women via technological innovation. It provides a proactive approach to prevent violence against women and save lives. The research emphasizes the significance of combining hardware and software in order to provide a complete safety tool. It also illustrates the vital role that quick support plays and the potential of such gadgets to promote confidence among women in public places. © 2024 IEEE.

关键词： Microcontrollers

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Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence 4

Perioperative Anesthesia Data: Visualization, Effects, Analy...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Mobile Digital Hospital System School of Electronics Peking University Ministry of Education Engineering Research Center Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350360721

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction. © 2024 IEEE.

关键词： Machine learning

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Ecosystem of Technologies for Smart Agriculture to Improve the Efficiency and Profitability of Indian Farmers 10

Ecosystem of Technologies for Smart Agriculture to Improve t...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sugandh, Urvashi Nigam, Swati Khari, Manju Banasthali Vidyapith Faculty of Mathematics and Computing Department of Computer Science Banasthali India School of Computing Science Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computer and System Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9789380544472

An increasing population demands more food, but the available resources are shrinking. The agriculture sector in India is facing several challenges such as climate change, water scarcity, soil degradation, and declining farm profitability. To address these challenges, there is a growing need for the adoption of smart agriculture practices that leverage technology. This paper presents an overview of the ecosystem of technologies for smart agriculture and their potential to improve the efficiency and profitability of Indian farmers. The paper explores the role of various technologies such as precision agriculture, IoT, artificial intelligence, drones, and robotics in improving crop yields, reducing input costs, and enhancing overall farm management. Additionally, the paper discusses the challenges that need to be overcome to ensure the successful adoption of smart agriculture technologies in India. These include the lack of awareness and access to technology, inadequate infrastructure, and limited availability of financing. The paper concludes that the ecosystem of technologies for smart agriculture holds significant promise for Indian farmers to overcome the challenges and improve the efficiency and profitability of their farms. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Profitability

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A Dense Retrieval system and Evaluation Dataset for Scientific Computational Notebooks 19

A Dense Retrieval System and Evaluation Dataset for Scientif...

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Li, Na Zhang, Yangjun Zhao, Zhiming Multiscale Networked System Informatics Institute University of Amsterdam Netherlands School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Amsterdam Netherlands

ISBN: (纸本)9798350322231

The discovery and reutilization of scientific codes are crucial in many research activities. Computational notebooks have emerged as a particularly effective medium for sharing and reusing scientific codes. Nevertheless, effectively locating relevant computational notebooks is a significant challenge. First, computational notebooks encompass multi-modal data comprising unstructured text, source code, and other media, posing complexities in representing such data for retrieval purposes. Second, the absence of evaluation datasets for the computational notebook search task hampers fair performance assessments within the research community. Prior studies have either treated computational notebook search as a code-snippet search problem or focused solely on content-based approaches for searching computational notebooks. To address the aforementioned difficulties, we present DeCNR, tackling the information needs of researchers in seeking computational notebooks. Our approach leverages a fused sparse-dense retrieval model to represent computational notebooks effectively. Additionally, we construct an evaluation dataset including actual scientific queries, computational notebooks, and relevance judgments for fair and objective performance assessment. Experimental results demonstrate that the proposed method surpasses baseline approaches in terms of F1@5 and NDCG@5. The proposed system has been implemented as a web service shipped with REST APIs, allowing seamless integration with other applications and web services. © 2023 IEEE.

关键词： Web services

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Discriminative feature encoding for intrinsic image decomposition

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Computational Visual Media 2023年第3期9卷 597-618页

作者： Zongji Wang Yunfei Liu Feng Lu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

Intrinsic image decomposition is an important and long-standing computer vision *** an input image,recovering the physical scene properties is *** physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image *** work takes advantage of deep learning,and shows that it can solve this challenging computer vision problem with high *** focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input *** achieve this goal,we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding *** define feature distribution divergence to efficiently separate the feature vectors of different intrinsic *** feature distributions are also constrained to fit the real ones through a feature distribution *** addition,a data refinement approach is provided to remove data inconsistency from the Sintel dataset,making it more suitable for intrinsic image *** method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent *** results indicate that our proposed network structure can outperform the existing state-of-the-art.

关键词： intrinsic image decomposition deep learning feature distribution data refinement

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A Coarse to Fine Detection Method for Prohibited Object in X-ray Images Based on Progressive Transformer Decoder 24

A Coarse to Fine Detection Method for Prohibited Object in X...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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An Adaptive Deep Learning Model for Crop Yield Prediction 2

An Adaptive Deep Learning Model for Crop Yield Prediction

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gadupudi, Ashwin Rani, Rudra Yamini Jayaram, B. Sharma, Nipun Deshmukh, Jotiram Krishna Reddy, Pundru Chandra Shaker California State University Computer Science Fresno United States Vnr Vignana Jyothi Institute of Engineering and Technology Information Technology Hyderabad India Rmk Engineering College Computer Science and Business System Tamil Nadu Kavaraipettai India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

Agriculture is the study and practice of raising plants and animals for human consumption. Approximately 60.45% of India's land is devoted to farming, making it the country's second largest economic sector. Agriculture and the related agro-industrial sector are crucial to India's economy. Soil components (such as nitrogen-phosphorus &potassium), crop rotation, soil-moisture, air and soil-temperatures, rainfall, and other environmental factors all play important roles in successful farming. One piece of current evidence in this area is a technique that utilizes machine-learning (ML) strategies like Random-Forest (RF), Decision-Tree (DT), and ANN to pick the best crop. In this study, we employ deep learning approaches to improve the suggested model, and together with crop prediction, we obtain clear information regarding the quantities of soil attributes requisite, along with their individual costs. It's more precise than the current model. By analyzing the data provided, it aids farmers in making informed business decisions. Predicting a proper yield requires thinking about the land's climate and soil. The goal is to introduce a Python-based system that makes use of strategic thinking to anticipate the most productive harvest under specific conditions while keeping costs to a minimum. While LSTM &RNN are employed as deep-learning((DL)-strategies in this work, SVM is used as the ML-strategy. © 2024 IEEE.

关键词： Forecasting

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception 27

Advswap: Covert Adversarial Perturbation with High Frequency...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2024 IEEE.

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