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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Xiaoyu Long, Jiangxuan Song, Zhao Zhou, Tianyi Stevens Institute of Technology Department of Computer Science HobokenNJ United States South China University of Technology School of Software Engineering Guangzhou China University of California Berkeley The Simons Institute for the Theory of Computing BerkeleyCA United States University of Southern California Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350362480

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n3) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ2) respectively, where m = Ω(n4) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ2). © 2024 IEEE.

关键词： Trees (mathematics)

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Glycan Immunogenicity Prediction with Efficient Automatic Graph Neural Network

Glycan Immunogenicity Prediction with Efficient Automatic Gr...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Chen, Jiamin Wu, Zhenpeng Gao, Jianliang Hu, Xiaohua Central South University School of Computer Science and Engineering Changsha China Drexel University College of Computing & Informatics United States

ISBN: (纸本)9781665468190

Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance. © 2022 IEEE.

关键词： Graph neural networks

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Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks 2

Automated Injury Detection and Alert Systems in Public Trans...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimal, S.P. Peter, John Benito Jesudasan Kavitha, U. Bhuvaneswari, G. Manikandan, G. Thamizhamuthu, R. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Capital One Services Llc RichmondVA United States Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology School of Commerce Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers' physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS' use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system's excellent damage detection accuracy is due to CNNs' advanced image processing and pattern recognition skills and IoT devices' sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Application and Issues of Blockchain Technology 5

Application and Issues of Blockchain Technology

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Dhami, Ankita Pareek, Prabhav Sachin Maurya, Sudhanshu Matta, Priya Rawat, Vandana Manu, Mehul School of Computing Graphic Era Hill University Nainital India Department of Cse Pune India Tula's Institute Department of Computer Science & Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Graphic Era Hill University Department of Allied Sciences Nainital India

ISBN: (纸本)9798350321425

The concept of blockchain technology has been spreading like wildfire since it was first proposed in the year 2008. In this research, core study is carried on blockchain working while aiming to collect all the necessary and bold information about blockchain technology available on the web. Objective of the study is to develop an understanding of the current research topic, benefits, importance, and future of blockchain from the technical perspective. Several research studies and several databases are analysed to finally conclude the content of this review study. Proposed research mainly focuses on blockchain technology and its importance. © 2023 IEEE.

关键词： Blockchain

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A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand 4

A Decision Support System using Machine Learning -IoT to tac...

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4th International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Negi, Harendra Singh Dimri, Sushil Chandra Kumar, Bhawnesh Kumar, Naveen Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9781665474368

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

关键词： Decision support systems

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Preface to the 13th Workshop on Bibliometric-enhanced Information Retrieval at ECIR 2023 13

Preface to the 13th Workshop on Bibliometric-enhanced Inform...

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13th International Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2023

作者： Frommholz, Ingo Mayr, Philipp Cabanac, Guillaume Verberne, Suzan Brennan, Jordan School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom GESIS Leibniz-Institute for the Social Sciences Cologne Germany University of Toulouse Computer Science Department IRIT UMR 5505 France Leiden Institute of Advanced Computer Science Leiden University Netherlands

This preface summarizes the 13th Workshop on Bibliometric-enhanced Information Retrieval (BIR). BIR 2023 was held as hybrid event at April 2nd, 2023, co-located with the 45th European Conference on Information Retriev...

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OTAS: An Elastic Transformer Serving System via Token Adaptation

OTAS: An Elastic Transformer Serving System via Token Adapta...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jinyu Chen Wenchao Xu Zicong Hong Song Guo Haozhao Wang Jie Zhang Deze Zeng Department of Computing The Hong Kong Polytechnic University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Computer Science China University of Geosciences

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requirements severely challenge current transformer serving systems, which rely on pre-training multiple variants of a foundation model, i.e., with different sizes, to accommodate varying service demands. Unfortunately, such a mechanism is unsuitable for large transformer models due to the additional training costs and excessive I/O delay. In this paper, we introduce OTAS, the first elastic serving system specially tailored for transformer models by exploring lightweight token management. We develop a novel idea called token adaptation that adds prompting tokens to improve accuracy and removes redundant tokens to accelerate inference. To cope with fluctuating query loads and diverse user requests, we enhance OTAS with application-aware selective batching and online token adaptation. OTAS first batches incoming queries with similar service-level objectives to improve the ingress throughput. Then, to strike a tradeoff between the overhead of token increment and the potentials for accuracy improvement, OTAS adaptively adjusts the token execution strategy by solving an optimization problem. We implement and evaluate a prototype of OTAS with multiple datasets, which show that OTAS improves the system utility by at least 18.2%.

关键词： Training Adaptation models Accuracy Current transformers Prototypes Production Throughput

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Clustering of Air Objects based on Path, Missile Path Prediction Using Deep Learning 2

Clustering of Air Objects based on Path, Missile Path Predic...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Surya Teja Gontumukkala, Sai Sai Varun Godavarthi, Yogeshwara Rama Ravi Teja Gonugunta, Bhanu Subramani, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

For a given instance, the location of the object in air can be demonstrated by quadruples there are latitude, altitude, longitude and velocity. Trajectory is continuous stream time, latitude, altitude and longitude. The proposed work classifies the air objects based on trajectories and group them into meaningful objects such as flying objects, drones, helicopters, fighter, civilian aircraft, unmanned aerial vehicles, missiles, flying bombs, and so on. In this paper, the proposed work estimates the location of air objects by using their trajectory and these provides a guidance to the missiles to shoot the target based on its location where the missile is going to meets the object based on the trajectory. For calculating missile's path, one of the most difficult challenges is applying the missile model to various related simulations is determining the missile path. The traditional way for this task is to use models and numerical methods, that also necessitates a large computational power. The data generated by the traditional system has been used to train the network and test the network, and the defect of the network prediction result is evaluated in this study. Recurrent neural network (RNN) and long short-term memory (LSTM) have been used to perform Clustering of Air Objects based on Path and for Missile Path Prediction. For clustering of air objects, LSTM gave r2 score of 0.997 and for missile path prediction, LSTM with 7 epochs gave an r2 score of 0.791. © 2022 IEEE.

关键词： Long short-term memory

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Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data

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computers, Materials & Continua 2022年第8期72卷 3665-3686页

作者： Mohammed BinJubier Mohd Arfian Ismail Abdulghani Ali Ahmed Ali Safaa Sadiq Faculty of Computing Universiti Malaysia PahangKuantanPahangMalaysia School of Computer Science and Informatics De Montfort UniversityLeicesterLE19BHUnited Kingdom School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonWulfruna Street WolverhamptonWV11LYUnited Kingdom

Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific ***,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published *** anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big ***,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership ***,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge *** study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data *** particular,the proposed method distributes the data into horizontal and vertical *** lower and upper protection levels are then used to identify the unique and identical attributes’*** unique and identical attributes are swapped to ensure the published big data is protected from disclosure *** outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.

关键词： Big data big data privacy preservation anonymization data publishing

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Development of Weather Forecast Application Using API

Development of Weather Forecast Application Using API

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computing, sciences and Communications (ICCSC), International Conference on

作者： Shikha Chadha Salman Khursheed Ahmad Dolly Sharma Manu Singh Anubhava Srivastavaline Department of Computer Science Sharda University Greater Noida India Department Of Computer Science Engineering School Of Computer Science & Engineering Galgotias University Greater Noida India School of Computing Department Graphic Era Hill University Haldwani India Department of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

This research paper presents the development of a weather forecasting model that incorporates real-time data through Application Programming Interfaces. This model utilises simple algorithms to analyse meteorological parameters from diverse API data sources, aiming to improve forecast accuracy compared to traditional methods. Extensive datasets are used for training and validation, demonstrating the model's effectiveness in providing more precise predictions. The study highlights the significance of API integration in advancing weather forecasting methodologies and fostering innovation in the field. The model created has yielded highly reliable results with minimal error.

关键词： Training Technological innovation Computational modeling Soft sensors Weather forecasting Predictive models Data models Real-time systems Reliability Application programming interfaces

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