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Towards Redefining the Reproducibility in Quantum computing: A data Analysis Approach on NISQ Devices

Towards Redefining the Reproducibility in Quantum Computing:...

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Quantum computing and Engineering (QCE), IEEE international conference on

作者： Priyabrata Senapati Zhepeng Wang Weiwen Jiang Travis S Humble Bo Fang Shuai Xu Qiang Guan Department of Computer Science Kent State University Department of Electrical and Computer Engineering George Mason University Quantum Science and Engineering Center George Mason University Quantum Science Center Oak Ridge National Laboratory Pacific Northwest National Laboratory HPC Group Department of Computer and Data Sciences Case Western Reserve University

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. The reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. Therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models' test accuracy and quantum computer hardware's calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications.

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Fruit Type Classification using Stacked Bi-directional Long Short Term Memory

Fruit Type Classification using Stacked Bi-directional Long ...

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Evolutionary algorithms and Soft computing Techniques (EASCT), international conference on

作者： Subba Reddy Borra Venkata Ramana Kaneti Vontela Tejaswini S Anitha Vinaykumar R IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In recent years, Machine Learning (ML) and Deep Learning (DL) applications have become increasingly important in agriculture, particularly in the classification of fruits based on image data. Fruit recognition and categorization have attained a lot of attention from researchers. Because of the diversity and heterogeneity of fruits, this is a difficult challenge. To solve this, a Stacked Bi-LSTM image classification approach has been proposed to enhance the accuracy of fruit image classification. The process includes feature extraction by a Convolutional Neural Network (CNN), followed by feature selection using a Recurrent Neural Network (RNN). Following that, the proposed Stacked Bi-LSTM model is utilized to classify fruit images, resulting in enhanced accuracy. As indicated by evaluation parameters like as recall, precision, and F-measure, the acquired results outperform previous algorithms. The analyzed outcomes show that the Stacked Bi-LSTM model accomplishes precision of 85.4%, recall of 88.3%, and F1-score of 83.9%, demonstrating greater performance when compared to existing approaches.

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Unlocking Black-Box Prompt Tuning Efficiency via Zeroth-Order Optimization

Unlocking Black-Box Prompt Tuning Efficiency via Zeroth-Orde...

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2024 conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhan, Heshen Chen, Congliang Ding, Tian Li, Ziniu Sun, Ruoyu The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798891761681

Prompt optimization emerges as an important technique for adapting Large Language Models (LLMs) to specific tasks. Unfortunately, LLM proprietors often limit access to models' internal weights, confining users to inference API services. This restriction poses a significant challenge for prompt optimization, as conventional optimization-based algorithms rely heavily on gradient information, which is unavailable via inference APIs. Addressing this challenge, this paper presents the Zeroth-Order Tuning (ZOT) approach, which enables efficient prompt tuning solely via inference APIs. ZOT adopts the zeroth-order optimization framework, utilizing finite differences to approximate gradient information. We further incorporate ZOT with gradient clipping and momentum techniques to enhance the tuning effectiveness. Experimental results show that ZOT outperforms existing black-box prompt tuning methods in terms of both task-specific performance and convergence speed. Furthermore, we provide a theoretical explanation for the unexpectedly strong performance of zeroth-order methods on LLM prompt tuning. By introducing the concept of effective dimension, we establish a strong connection between the inherently low effective dimension of prompt spaces and the superior convergence speed of zeroth-order methods. Our code is available at https://***/ZhanHeshen/ZOT. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Classification and prediction of ECG data based on decision trees and their optimization algorithms

Classification and prediction of ECG data based on decision ...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Runqiang Xing Xi’an Technological University Xi’an China

The heart beat is usually synchronized with the electrical activity of the heart, and the ECG records the representation of the heart's electrical signals on the body surface. In this study, we developed a classification algorithm model for cardiac arrhythmias by analyzing ECG data. We first constructed a classification model to determine the danger level of ECG data using a decision tree approach to achieve real-time judgment and alarm of arrhythmias. The model was then post-pruned and optimized to further classify and predict specific categories for each risk level in conjunction with the actual situation, resulting in an algorithmic model with relatively high prediction accuracy. This will provide an important monitoring tool for cardiac patients and promote timely treatment.

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A Real TimeVehicle Detection Based on Modified Convolutional Neural Networks in Different Climate

A Real TimeVehicle Detection Based on Modified Convolutional...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Sandra Johnson B Prathusha Laxmi Department of Artificial Intelligence and Data Science R.M.K. Engineering College Chennai Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai

In order to effectively manage and monitor traffic on the roads, intelligent technologies must be able to detect and tally the number of cars on the road. The technology may be used for controlling and monitoring traffic in both urban areas and on highways, especially in times of heavy congestion or bad weather. It has traditionally been accomplished using sensor data and the standard toolkit for image processing. Recently however, with the help of deep learning-based smart computer vision systems, this process has become highly scalable and dependable. For intelligent traffic monitoring and management of issues like traffic congestion, the information gathered from various surveillance cameras may be utilized to train models that can recognize and track on-road cars, even in low-light and blurry situations brought on by bad weather. Although they all aim to solve the same problem, vehicle identification algorithms all have to deal with one or two very distinct cases. Vehicle detection under haze, dust, sandstorms, snow, and rain, as well as daytime and nocturnal conditions, is the focus of this study. The suggested design is based on the CNN architecture as a starting point, but it adds a spatial pyramid pooling layer and removes several of the Batch Normalization levels. During training, the system achieved an average accuracy of 81% and was able to identify even the tiniest vehicles in the scene.

关键词： Training Roads Vehicle detection Surveillance Urban areas Traffic control Convolutional neural networks

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GAPter: Gray-Box data Protector for Deep Learning Inference Services at User Side

GAPter: Gray-Box Data Protector for Deep Learning Inference ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hao Wu Bo Yang Xiaopeng Ke Siyi He Fengyuan Xu Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University

The widespread deployment of Deep Learning Inference Services (DLISes) has raised people’s concerns about their data privacy being breached. Although data privacy enhancement has recently attracted a lot of attention, existing solutions all require the cooperation of service providers. Users lose control of their data when making data privacy enhancement decisions. However, it is difficult to enable the user-side control of data abuse prevention because users do not have any programming skills, deep learning knowledge, or rich computing resources. In this work, we propose a fully-automatic userside data privacy enhancement solution, GAPter, for DLISes. Given such a DLIS, GAPter can adaptively fuzz the service for a suitable enhancement strategy, with no cooperation between the DLIS provider and the user. We have implemented and comprehensively evaluated GAPter. The experimental results show that GAPter can find good balance points between privacy enhancement and user data utility.

关键词： Deep learning data privacy Automation Quality of service Signal processing Programming Fuzzing

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Parallel computing Approach for Preprocessing AIS data in Transshipment Feature Extraction

Parallel Computing Approach for Preprocessing AIS Data in Tr...

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international conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)

作者： Widyadi Setiawan Linawati Linawati I Made Oka Widyantara Dewa Made Wiharta Putu Arya Mertasana Sri Andriati Asri Doctoral Program of Engineering Science Udayana University Denpasar Indonesia Electrical Engineering Departement Udayana University Denpasar Indonesia Electrical Engineering Departement Politeknik Negeri Bali Denpasar Indonesia

AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent data processing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes data processing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality.

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Convolutional Neural Networks For Eye Detection Trained With Manually And Automatically Generated Ground Truth data

Convolutional Neural Networks For Eye Detection Trained With...

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international Symposium on Symbolic and Numeric algorithms for Scientific computing (SYNASC)

作者： Sorin Valcan Mihail Găianu Department of Computer Science West University of Timişoara and Continental Automotive Romania - User Experience UX Timişoara Romania

Eye detection represents an essential facial feature to be detected in driver monitoring systems representing the basis for further processing for attention or drowsiness detection. The machine learning approach for infrared image vision problems comes with multiple obstacles like creating a recordings data set with drivers and labeling it to be able to train a model. In very strict areas like automotive or medical imaging machine learning approaches are still a big debate especially because of the black box that is represented by the model, meaning that a wrong detection is impossible to be predicted or explained. That’s why the entire focus is shifted to control the data set and the labeling process with very hard manual effort. This paper presents the experimental results of training convolutional neural networks for eye detection in automotive industry using ground truth data obtained from an automatic labeling module. These results are compared with neural networks that were trained using the same data set but with labels created by manual human effort. This experiment shows that similar accuracy of convolutional neural network can be obtained in image vision problems without manual work for image labeling.

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Enhancing Big data Management for Elderly Care Through a Blockchain-Empowered Deep Reinforcement Learning Model 7th

Enhancing Big Data Management for Elderly Care Through a Blo...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Shixiao, Xiao Goyal, S.B. Rajawat, Anand Singh Solanki, Ram Kumar Chengyi University College Jimei University Xiamen361021 China Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computers Science and Engineering Sandip University Nashik422213 India

ISBN: (纸本)9783031733239

The surging demand for elderly care services, propelled by the global population’s demographic shift, necessitates innovative approaches for efficient data management and decision-making. This paper introduces a novel framework that synergizes blockchain technology with a deep reinforcement learning (DRL) model to elevate the integrity, security, and effectiveness of big data management in elderly care. Our proposed model offers dual benefits: the decentralized nature of blockchain ensures the secure storage and exchange of patient data, countering potential breaches and unauthorized accesses. This is particularly crucial given the sensitive nature of elderly patients’ information. Additionally, using smart contracts, automated and transparent caregiving decisions can be executed with-out intermediaries, thus ensuring trust among stakeholders. On the other hand, the DRL model dynamically learns and adapts to individual elderly care requirements by processing vast amounts of data, offering personalized care recommendations. By integrating it with blockchain, we not only ensure data integrity but also optimize decision-making in real-time, accommodating the evolving needs of the elderly population. A series of experiments were conducted using real-world data to validate the effectiveness of our proposed model. Results highlighted significant improvements in data security, reduction in fraudulent activities, and efficient, tailored caregiving decisions. The holistic integration of blockchain and DRL presents a revolutionary stride in elderly care, offering an optimized, secure, and personalized caregiving paradigm. This paper also discusses the potential challenges of implementing this model and offers directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep reinforcement learning

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Research on Image processing of Big data Based on Computer Vision

Research on Image Processing of Big Data Based on Computer V...

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Internet of Things, Robotics and Distributed computing (ICIRDC), international conference on

作者： Cui Wang Yuanyuan Wang Weifang Engineering Vocational College Weifang China

With the advent of the era of big data, the image processing of big data based on Computer Vision (CV) has become a research field of great concern. The purpose of this study is to explore how to improve image processing methods through deep learning technology to better adapt to large-scale and high-dimensional image data. We propose an innovative image processing framework, based on the improved ResNet50, and introduce multi-scale feature fusion, attention mechanism and width adaptation strategies. In the task of image classification, our method has achieved remarkable improvement in accuracy, precision, recall and FI value. Through the effective fusion of multi-scale features, the model better captures the abstract information in the image and improves the classification accuracy. In the target detection task, our method shows excellent performance in mAP, false alarm rate and so on. The improved ResNet50, which introduces attention mechanism, is more reliable in identifying the target position and category. The experimental results further prove the innovation and practicability of our proposed method. Compared with the traditional model and the latest image processing methods, our method shows significant advantages in big data image processing. This provides new ideas and technical support for solving large-scale and diversified image data processing problems. The contribution of this study is to provide a new solution for big data image processing based on CV. The improved ResNet50 model has made remarkable progress in performance, which opens up a new research direction for deep learning research in the field of big data image processing.

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