检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Aharonov, Dorit Alberton, Ori Arad, Itai Atia, Yosi Bairey, Eyal Brakerski, Zvika Cohen, Itsik Golan, Omri Gurwich, Ilya Kenneth, Oded Leviatan, Eyal Lindner, Netanel H. Melcer, Ron Aharon Meyer, Adiel Schul, Gili Shutman, Maor Qedma Quantum Computing Tel Aviv Israel The Benin School of Computer Science and Engineering Hebrew University Jerusalem Israel Centre for Quantum Technologies National University of Singapore Singapore Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel Department of Physics Technion Haifa Israel

Quantum error mitigation (EM) is a family of hybrid quantum-classical methods for eliminating or reducing the effect of noise and decoherence on quantum algorithms run on quantum hardware, without applying quantum error correction (EC). While EM has many benefits compared to EC, specifically that it requires no (or little) qubit overhead, this benefit comes with a painful price: EM seems to necessitate an overhead in quantum run time which grows as a (mild) exponent [1–4]. Accordingly, recent results show that EM alone cannot enable exponential quantum advantages (QAs), for an average variant of the expectation value estimation problem [5]. These works raised concerns regarding the role of EM in the road map towards QAs. We aim to demystify the discussion, and provide a clear picture of the role of EM in achieving QAs, both in the near and long term. We first propose a clear distinction between finite QA and asymptotic QA, which is crucial to the understanding of the question, and present the notion of circuit volume boost, which we claim is an adequate way to quantify the benefits of EM. Using these notions we can argue straightforwardly that EM is expected to have a significant role in achieving QAs. Specifically, that EM is likely to be the first error reduction method for useful finite QAs, before EC;that the first such QAs are expected to be achieved using EM in the very near future;and that EM is expected to maintain its important role in quantum computation even when EC will be routinely used – for as long as high-quality qubits remain a scarce resource. © 2025, CC BY.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Mitigating ML Model Decay in Continuous Integration with Data Drift Detection: An Empirical Study

arXiv

引用

arXiv 2023年

作者： Arani, Ali Kazemi Babar, Muhammad Ali Le, Triet Huynh Minh Zahedi, Mansooreh CREST-The Centre for Research on Engineering Software Technologies School of Computer Science University of Adelaide Adelaide Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

Background: Machine Learning (ML) methods are being increasingly used for automating different activities, e.g., Test Case Prioritization (TCP), of Continuous Integration (CI). However, ML models need frequent retraining as a result of changes in the CI environment, more commonly known as data drift. Also, continuously retraining ML models consume a lot of time and effort. Hence, there is an urgent need of identifying and evaluating suitable approaches that can help in reducing the retraining efforts and time for ML models used for TCP in CI environments. Aims: This study aims to investigate the performance of using data drift detection techniques for automatically detecting the retraining points for ML models for TCP in CI environments without requiring detailed knowledge of the software projects. Method: We employed the Hellinger distance to identify changes in both the values and distribution of input data, and leveraged these changes as retraining points for the ML model. We evaluated the efficacy of this method on multiple datasets and compared the APFDc and NAPFD evaluation metrics against models that were regularly retrained, with careful consideration of the statistical methods. Results: Our experimental evaluation of the Hellinger distance-based method demonstrated its efficacy and efficiency in detecting retraining points and reducing the associated costs. However, the performance of this method may vary depending on the dataset. Conclusions: Our findings suggest that data drift detection methods can assist in identifying retraining points for ML models in CI environments, while significantly reducing the required retraining time. These methods can be helpful for practitioners who lack specialized knowledge of software projects, enabling them to maintain ML model accuracy. © 2023, CC BY.

关键词： Transmission control protocol

来源：评论

学校读者我要写书评

暂无评论

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

引用

International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Using Variational Quantum Circuit 1

引用

11th International Advanced computing Conference, IACC 2021

作者： Karur Mudugal Mathad, Rajashekharaiah Saurabh, Abhishek Mishra, Aditya Jain, Sambhav Kumar, Purushottam Vardaan Chickerur, Satyadhyan School of Computer Science and Engineering K L E Technological University Karnataka Hubballi580 031 India Centre for High Performance Computing K L E Technological University Karnataka Hubballi580 031 India

ISBN: (数字)9783030955021

ISBN: (纸本)9783030955014

Quantum computing and particularly quantum machine learning has witnessed a surge in the direction of fruitful research being done in recent years. Variational quantum algorithms have gained popularity due to their resilience to error, which is a common occurrence in the near term intermediate scale quantum (NISQ) hardware. In such an algorithm, an optimal function is mapped by a quantum circuit which is designed using weighted parameterized gates to be trained based on a cost function that is optimized using gradient descent. A Variational Quantum Classifier (VQC) is such an algorithm and is used for supervised learning to perform data classification. But since the quantum hardware available today is not powerful enough for a VQC to come of any practical use in the current setting, we implement a transfer learning pipeline in which a pre-trained image processing model is fine tuned using a dressed VQC;for a specific, binary classification task. Our aim was to study the working of a VQC and test it’s behaviour with respect to multiple hyperparameters such as circuit depth, number of qubits, etc. We trained our transfer learning-VQC model for 4 different datasets and for different hyperparameters and were able to get close to state of the art performance in most of our tests, solidifying the prospect of VQC as an efficient quantum-classical hybrid supervised learning algorithm. It should be noted that the study of VQC was chosen over that of other algorithms available for classification because recent advancements in the understanding of VQCs that show promise in favour of the algorithm. © 2022, Springer Nature Switzerland AG.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Personalized Precision Medicine for Cancer Treatment

A Deep Learning Approach for Personalized Precision Medicine...

引用

Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Kamal Upreti Sambhajiraje Patil Gaurav Agrawal Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science CHRIST (Deemed to be University) Ghaziabad India Centre for Online Learning Dr. D. Y. Patil Vidyapeeth Pune India Department of Computer Science Engineering (AI) KIET Group of Institution Ghaziabad India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Current research combines multiple machine learning models to provide comprehensive insights into tailor-made medicine for lung cancer. The study identified micropatterns of malignant disorders using a dataset of 3222 CT and MRI scans using VGG16 architecture, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). A comprehensive understanding of the characteristics of pulmonary arteries is obtained. After extensive evaluation, the models exhibit exceptional accuracy and precision, with VGG16 performing particularly well. Confusion matrices provide comprehensive insight into model predictions. This work provides important new information in the field of personalized precision medicine through the potential of standardized methods for lung cancer diagnosis and treatment. Future research should focus on sample design they will be enhanced, and their application will be extended to multiple datasets.

关键词： Support vector machines Accuracy Precision medicine Magnetic resonance imaging Computed tomography Confusion matrices Lung cancer Nearest neighbor methods Feature extraction Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Online demand peak shaving with machine-learned advice in digital twins

Digital Twins and Applications

引用

Digital Twins and Applications 2024年第1期1卷 38-50页

作者： Minxi Feng Wei Li Boyu Qin Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneySydneyNew South WalesAustralia State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical EngineeringXi'an Jiaotong UniversityXi'anChina

As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin *** load management is central to energy systems within digital twins,which significantly impacts operational *** demand loads can lead to substantial monthly utility expenses without proper ***,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand *** leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load *** provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive *** trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.

关键词： operations maintain battery

来源：评论

学校读者我要写书评

暂无评论

Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Cryptocurrency Price Prediction and Analysis

Cryptocurrency Price Prediction and Analysis

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Vishal Shekhar Baviskar Radha D. S S Uma Sankari Department of Computer Science and Engineering Amrita School of Computing Bangalore India Vikram Sarabhai Space Centre Indian Space Research Organisation Bangalore India

Cryptocurrencies have become a major element in enterprises and financial market showing promising potential during the past ten years. Predictions that are accurate can help cryptocurrencies investors to make the best decisions and possibly enhance their earnings. For this work 4 cryptocurrencies namely Bitcoin, Binance Coin, Ethereum and Tether (USDT) are considered. The considered dataset is for around 5 years, which has a total of 9 columns, One Date Column ranging from year 2017 to 2022 and for each of the Coin there is the Closing price and the Volume. The Dataset is collected from Kaggle. Attempts are made to build the models in various ways, by dealing with certain features or by taking a subset of the dataset. All the models, according to the way the dataset was dealt were evaluated based on certain evaluation metrics like R-squared, mean square error and root mean square error. The models used are KNN, Decision Tree, Random Forest, XGBoost and CatBoost. Visualized and understood how the prices of those cryptocurrencies were impacted by other features of the dataset. The models that outperformed are Random Forest and CatBoost. Achieved RMSE was 365, 2.99, 29.99 and 0.0023 for Bitcoin, Binance, Ethereum and USDT respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Authentication-Based Image Scrambling Algorithm Using Hybrid Chaotic Maps and SHA

An Authentication-Based Image Scrambling Algorithm Using Hyb...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： B. Ramneshkar R. Bala Krishnan N.Rajesh Kumar B. Karthikeyan G. Manikandan School of Computing SASTRA Deemed University Thanjavur India Department of Computer Science and Engineering Srinivasa Ramanujan Centre SASTRA Deemed University Kumbakonam India

Digital mode of communication is widely used for exchanging various kinds of data through internet. The data that is being transmitted may be used in sensitive applications. Thus, those data have to be secured from various kinds of attacks and must ensure basic security services as well. One such kind of data is Image data which is used in medical imaging and some other sensitive fields as well. There are many algorithms to protect and secure the images during transmission but the algorithm and their security has to be modified constantly to cope up with the technological advancements. So, in this enhanced work, an authentication-based image scrambling algorithm using hybrid chaotic maps and SHA is proposed. RSA provides the initial-values for the chaotic-map. Improved LS will be used to generate random values for XOR operation. SBOX will be generated by using both Improved LS and Henon map. Authentication message will be embedded into the image and it will be XORed and scrambled thrice (SBOX, Arnold and Sub-Image based). Receiver will decrypt and verify the message. This algorithm is expected to be faster, provide good security and resist several attacks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fan, Shitong Xiao, Feiyang Wang, Wenbo Qi, Shuhan Zhu, Qiaoxi Wang, Wenwu Guan, Jian Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE.

关键词： Acoustic-based traffic monitoring feature fusion graph attention pre-trained model transfer learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：