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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Jingyu Tian, Jiachen Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Systems Engineering National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350319934

Hadoop is currently the most popular big data processing architecture, which provides a processing framework for managing and analyzing massive data. Hadoop Distributed File System (HDFS) is the core component for storing data in the Hadoop systems. It performs well when storing and managing large-sized files. However, it shows extremely bad performance when dealing with a large number of small files, which is reflected in the consumption of a large amount of memory space of the NameNode node and the low efficiency of accessing small files. Archive file is proposed to solve this problem. It combines small files into larger merged files for storage, and uses index files to record the relevant information of small files in the merged files. However, archive files (e.g., Hadoop Archive, MapFile) all require additional processing and multiple I/O operations to obtain index information before actually accessing the file content, which reduces the efficiency of file access. This paper proposes a novel distributed file storing mechanism called B+ Tree-Based Distributed File Storing mechanism (BTDFS), which combines a large number of small files into a merged file, records the index information of the small files in the merged file, and organizes the index information into a B+ tree for storage. Using BTDFS, while reducing the metadata consumption of the NameNode node, improves the access performance of a single small file with additional abilities, which provides convenience for analyzing a large number of small files. Finally, this paper designs corresponding experiments for the proposed BTDFS, and tests and analyzes the memory usage in N ameN ode and the access performance of small files. The experimental results show that the proposed BTDFS can effectively reduce the memory usage of the NameNode, and the efficiency of accessing small files is improved compared with state-of-the-art methods. © 2022 IEEE.

关键词： Digital storage

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Integration of a Hybrid Model with XAI for Stock Price Forecasting: A Dashboard-Driven Approach

Integration of a Hybrid Model with XAI for Stock Price Forec...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jagannathan J Veera Venkata Shivamani Krishna Goditi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This paper on Integration of a Hybrid Model with XAI for Stock price Forecasting focuses on improving the accuracy and transparency of stock price predictions through a combined model of Long Short-Term Memory (LSTM) and MultiProphet, with an emphasis on explainable artificial intelligence (XAI). LSTM which is a deep learning model, is good at identifying patterns in time-series data. while MultiProphet which is an enhanced version of Facebook's Prophet model, effectively handles seasonal variations and anomalies in financial data. By integrating both models, the hybrid approach delivers more reliable stock forecasting. In addition, this project integrates XAI techniques like SHAP and LIME to offer greater insight into how the model generates predictions, which is crucial for informed financial decision-making. Dynamic dashboard provides an intuitive interface for users to track stock price trends, view forecasts, and access explanations of the model's predictions in real-time. This combination of improved prediction accuracy and model transparency enables better decision-making by stakeholders, who can base their actions on clear and interpretable information. Overall, the project represents a significant advancement in making advanced AI models more accessible and actionable in practical, user-oriented financial applications.

关键词： Analytical models Technological innovation Accuracy Explainable AI Decision making Predictive models Market research Data models Forecasting Long short term memory

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Analyzing Adaptive End-to-End Congestion Control Strategies for Underwater High Speed Communication Networks 3

Analyzing Adaptive End-to-End Congestion Control Strategies ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mythili, V. Shinde, Gitanjali R. Swarup, Lakshya Yadav, Dhananjay Sinha, Deepak Kumar Kumar, Rohit Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper investigates modern-day Adaptive stop-to-give up (AEE) congestion manage techniques for underwater high velocity verbal exchange networks. AEE techniques have the capability to provide a greater efficient and strong transmission for underwater acoustic channels. in this paper we examine the one-of-a-kind AEE congestion manage strategies, their overall performance blessings and limitations. We moreover talk the exchange-offs between a couples of criteria, including strength intake, through-placed and put off. A complete analysis of current strategies is made and a comprehensive summary of the relative performance of the techniques are furnished. finally, destiny studies instructions are mentioned, to bridge the gap between theoretical fashions and real-time structures. © 2023 IEEE.

关键词： acoustic fashions performance structures

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Symbolic-Numeric Co-Simulation of Josephson Traveling-Wave Parametric Amplifiers 19

Symbolic-Numeric Co-Simulation of Josephson Traveling-Wave P...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Haider, Michael Yuan, Yongjie Stowasser, Johannes Seitner, Lukas Elkin, Samuel T. Roth, Thomas E. Jirauschek, Christian Tum School of Computation Information and Technology Technical University of Munich Garching Germany Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette United States

ISBN: (纸本)9788831299107

Analytic solutions fail to accurately predict the gain and added quantum noise in traveling-wave parametric amplifiers for large signal amplitudes. Thus, the underlying system Hamiltonian needs to be extended to include more and more modes in order to describe effects such as spectral leakage. For a sufficiently complex Hamiltonian, the procedure of deriving the Heisenberg equations of motion for each mode is tedious or even prohibitive. Therefore, we present a symbolic-numeric co-simulation approach, where the equations of motion are automatically derived from a given Hamiltonian using a modern computer algebra system. The resulting equations of motion can be solved numerically. We verified our approach using existing analytic and numeric solutions from the literature and found good agreement. Additionally, we simulate an amplifier from the literature, where the pump mode experiences substrate losses beyond the stiff pump approximation. © 2025 European Association on Antennas and Propagation.

关键词： parametric amplifier quantum computing quantum theory traveling-wave parametric amplifier

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Multimodal depression detection using a deep feature fusion network 3

Multimodal depression detection using a deep feature fusion ...

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3rd International Conference on computer Science and Communication technology, ICCSCT 2022

作者： Sun, Guangyao Zhao, Shenghui Zou, Bochao An, Yubo School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer and Comunication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781510661240

Currently, more and more people are suffering from depression with the increase of social pressure, which has become one of the most severe health issues worldwide. Therefore, timely diagonosis of depression is very important. In this paper, a deep feature fusion network is proposed for multimodal depression detection. Firstly, an unsupervised autoencoder based on transformer is applied to derive the sentence-level embedding for the frame-level audiovisual features;then a deep feature fusion network based on a cross-modal transformer is proposed to fuse the text, audio and video features. The experimental results show that the proposed method achieves superior performance compared to state-of-the-art methods on the English database DAIC-WOZ. © 2022 SPIE.

关键词： Feature extraction

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DTTR: Detecting Text with Transformers 48

DTTR: Detecting Text with Transformers

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Jing You, Zhiqiang Zhong, Zhiwei Liu, Peng Mei, Langqi Huang, Shenguang Hunan University College of Computer Science and Electronic Engineering Changsha China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Ningbo Port Information Communication Co. Ltd. Ningbo China

ISBN: (纸本)9781728163277

Recently, most transformer-based approaches have achieved considerable success on vision tasks, even better than those with convolution neural networks (CNNs). In this paper, we present a novel transformer-based model, named detecting text with transformers (DTTR), for scene text detection. In DTTR, a CNN backbone extracts local connectivity features and a transformer decoder captures global context information from a scene text, effectively. In addition, we propose a dynamic scale fusion (DSF) module that can fuse multiscale feature maps dynamically, thus significantly improving the scale robustness and rendering powerful representations for subsequent decoding. Experimental results show that DTTR achieves 0.5% H-mean improvements and 20.0% faster in inference speed than the SOTA model with a backbone of ResNet-50 on MMOCR. Code will be released at: https://***/ahsdx/DTTR. © 2023 IEEE.

关键词： Decoding

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Rat Swarm Optimizer based Transform for Performance Improvement of Machine Learning Classifiers in Diagnosis of Lung Cancer 2

Rat Swarm Optimizer based Transform for Performance Improvem...

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2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Kavya, B. Meghana, G. Roshni, M. Bharanidharan, N. Dayananda Sagar University Department of Computer Science & Engineering Bangalore India Vellore Institute of Technology School of Information & Technology Engineering Vellore India

ISBN: (纸本)9781665460477

Usage of Machine Learning algorithms for assisting healthcare providers is increasing day by day. But the performance and robustness of the machine learning algorithms are the main concerns while implementing them for critical healthcare applications such as detection of cancer. This work concentrates on the performance improvement of supervised classifiers through the feature transform based on Rat Swarm Optimizer in diagnosing lung cancer using histopathological images. Rat Swarm Optimizer used for the transformation of features. These transformed features are more capable of providing better classification accuracy when compared to normal features. The dataset is downloaded from the publicly available website and three classes are present: normal, lung squamous cell carcinomas, and lung adenocarcinomas. In each class, 1000 histopathological images are considered. Four supervised classifiers namely Histogram-Gradient boosting classifier, Random forest classifier, K-Nearest Neighbor classifier, and Linear Discriminant Analysis classifiers are tested. The highest accuracy of 90.66% is offered by Histogram-Gradient boosting classifier and this is increased to 95.82% when Rat Swarm Optimizer is used as transform before classification. © 2022 IEEE.

关键词： Rats

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SlowFast with DropBlock and Smooth Samples Loss for Student Action Recognition 14

SlowFast with DropBlock and Smooth Samples Loss for Student ...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Li, Chuanming Bao, Wenxing Chen, Xu Jing, Yongjun Qu, Xiudong School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Computer and Information Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781510657564

Due to the advent of large-scale video datasets, action recognition using three-dimensional convolutions (3D CNNs) containing spatiotemporal information has become mainstream. Aiming at the problem of classroom student behavior recognition, the paper adopts the improved SlowFast network structure to deal with spatial structure and temporal events respectively. First, DropBlock (a regularization method) is added to the SlowFast network to solve the overfitting problem. Second, for the problem of Long-Tailed Distribution, the designed Smooth Sample (SS) Loss function is added to the network to smooth the number of samples. Classification experiments show that compared with similar methods, the model accuracy of our method on the Kinetics and Student Action Dataset is increased by 2.1% and 2.9%, respectively. © 2022 SPIE.

关键词： Students

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems Science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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Sector-wise Investigation of BYOD Security Policies in Pakistan 19

Sector-wise Investigation of BYOD Security Policies in Pakis...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Sajid, Ayesha Javed, Yousra School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan School of Information Technology Illinois State University United States

ISBN: (纸本)9781665461979

The growing use of personal devices at workplace due to the Bring Your Own Device (BYOD) paradigm has given rise to new security challenges. Several organizations within Pakistan have recently started adopting the BYOD approach. However, limited work has been carried out to investigate the current state of BYOD security policies in Pakistan. We use a survey-based study to explore the BYOD security policies within four industrial sectors of Pakistan namely, Defence Services, Finance, IT/Telecommunications, and Medical. We highlight the BYOD security issues and currently implemented counter-measures within these sectors, and propose a BYOD security framework and a set of sector-wise recommendations for dealing with the BYOD security issues. © 2022 IEEE.

关键词： Security systems

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