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CHSH Violations using Dynamic Circuits

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arXiv 2025年

作者： Waring, Jean-Baptiste Pere, Christophe Le Beux, Sébastien Dept. of Electrical & Computer Engineering Concordia University MontréalQC Canada Dept. of Computer Science & Software Engineering INTRIQ Laval University QuébecQC Canada PINQ2 Canada

Scalable quantum computing relies on high-quality, long-range entanglement, a challenge on noisy, near-term devices. The need for practical insights for near-term algorithm design calls for trade-offs exploration in implementing dynamic circuits on current hardware. In this work, we experimentally compare three CNOT implementations for generating Bell states across varying qubit separations on a 127-qubit IBM Quantum Eagle processor (ibm_ quebec): a unitary (SWAP-based) approach, a dynamic approach with mid-circuit measurements and classical feedforward, and a post-processed approach. We use Clauser-Horne-Shimony-Holt (CHSH) inequality violations to quantify entanglement quality. We observe that, beyond 10 qubits, dynamic circuits surpass the unitary approach, demonstrating improved distance-dependent entanglement preservation. The post-processed approach consistently yields the highest CHSH values, reaching |S| > 2 up to 13 qubits. Our results underscore the critical need for faster classical feedforward and higher readout fidelity. © 2025, CC BY-NC-SA.

关键词： Qubits

An Efficient Localization for Indoor Environment using Classification Algorithms

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An Efficient Localization for Indoor Environment using Class...

2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Mulahuwaish, Aos Towne, Wyatt Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Saginaw Valley State University Dept. of Computer Science and Information Systems Michigan United States Salahaddin University-Erbil Dep. of Software Engineering and Informatics Iraq Koya University Faculty of Engineering Dept. of Software Engineering Kurdistan Region-F.R. Iraq

ISBN: (纸本)9781728176246

The positioning accuracy of various kinds of devices is often calculated by using Wi-Fi fingerprints as a data source of Received Signal Strength (RSS). Through different matching algorithms, these RSS values are related to corresponding positioning coordinates for positioning estimation. In this paper, we compare two well known Machine Learning (ML) structures for calculating positioning accuracy: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) and a Decision Tree (DT). Both matching algorithms run through a series of datasets in two parts. In the first part, the datasets contain raw, non-normalized RSS values. In the second part, the RSS values are all normalized to remove noise in the datasets. After extensive testing and simulations, the tests showed that as long as RSS values are first normalized, a Decision Tree offers better average positioning accuracy. © 2020 IEEE.

关键词： Decision trees

Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin 5

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Method for filtering encrypted traffic using a neural networ...

5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Luksha, Ivan Duy Dinh, Truong Karelin, Evgenii Glushakov, Ruslan Kirichek, Ruslan Dept. of Software Engineering and Computer Science Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint-Petersburg Russia Faculty Information Technology 1 Posts and Telecommunications Institute of Technology Hanoi Viet Nam Saint-Petersburg State Pediatric Medical University Saint-Petersburg Russia

ISBN: (纸本)9781450387347

Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be "transparent"to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out. © 2021 ACM.

关键词： Machine learning

A 2-channel time-to-digital converter with 20-ps resolution for pico-second timing application

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A 2-channel time-to-digital converter with 20-ps resolution ...

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Zhouji Du Qin Ke Hang Yu Yan Li Binjie Ge Shanshan Yong Yonghong Cheng Shenzhen Laboratory of IC Design for IOT College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Electronics and Computer Engineering SHENZHEN MCU-BIT University Shenzhen China Shenzhen Graduate School Peking University Shenzhen China Hubble Sensor Solution Shenzhen Co. LTD Shenzhen China

ISBN: (纸本)9781665492331

Time-to-Digital Converter (TDC) provides picosecond accuracy timing and is widely used in many applications such as Light Detection and Ranging (LIDAR), 3-D vision, and ultrasonic flow sensors. This paper introduces a 2-channel TDC with two-level resolution control architecture. Designed in a 180 nm CMOS process, the TDC occupies an area of 2.3 mm $\times 1.3$ mm and achieves 20 ps resolution at 2 Msps measurement rate.

关键词： Laser radar Ultrasonic variables measurement Area measurement Delay lines CMOS process Distance measurement Acoustics

EnHMM: On the use of ensemble HMMs and stack traces to predict the reassignment of bug report fields

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arXiv 2021年

作者： Islam, Md Shariful Hamou-Lhadj, Abdelwahab Sabor, Korosh Koochekian Hamdaqa, Mohammad Cai, Haipeng Dept. Electrical and Computer Engineering Concordia University MontrealQC Canada Department of Computer and Software Engineering Polytechnique Montreal MontrealQC Canada School of Electrical Engineering and Computer Science Washington State University PullmanWA United States

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to ***, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of *** by 6.80% and improves the average recall of *** by 36.09%. However, the average precision of EnHMM is lower than that of *** (53.93% as opposed to 56.71%). © 2021, CC BY.

关键词： Hidden Markov models

SemParser: A Semantic Parser for Log Analytics

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arXiv 2021年

作者： Huo, Yintong Su, Yuxin Lee, Cheryl Lyu, Michael R. Computer Science & Engineering Dept The Chinese University of Hong Kong Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University Zhuhai China

Logs, being run-time information automatically generated by software, record system events and activities with their timestamps. Before obtaining more insights into the run-time status of the software, a fundamental step of log analysis, called log parsing, is employed to extract structured templates and parameters from the semi-structured raw log messages. However, current log parsers are all syntax-based and regard each message as a character string, ignoring the semantic information included in parameters and templates. Thus, we propose the first semantic-based parser SemParser to unlock the critical bottleneck of mining semantics from log messages. It contains two steps, an end-to-end semantics miner and a joint parser. Specifically, the first step aims to identify explicit semantics inside a single log, and the second step is responsible for jointly inferring implicit semantics and computing structural outputs according to the contextual knowledge base of the logs. To analyze the effectiveness of our semantic parser, we first demonstrate that it can derive rich semantics from log messages collected from six widely-applied systems with an average F1 score of 0.985. Then, we conduct two representative downstream tasks, showing that current downstream models improve their performance with appropriately extracted semantics by 1.2%-11.7% and 8.65% on two anomaly detection datasets and a failure identification dataset, respectively. We believe these findings provide insights into semantically understanding log messages for the log analysis community. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

A Study on the Prediction of Emotion from Image by Time-flow depend on Color Analysis

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A Study on the Prediction of Emotion from Image by Time-flow...

2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Lee, Taemin Lee, Nahyuk Seo, Sanghyun Kang, Dongwann Chung-Ang University Davinci Software Institute Seoul Korea Republic of Chung-Ang University Computer Science Engineering Seoul Korea Republic of Chung-Ang University School of Computer Art College of Art Anseong-Si Korea Republic of Seoul National University of Science and Technology Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9781728176246

When people are looking at a landscape, we feel different depending on time/season. This part is based on the colors of the landscape, even if you look at the same landscape due to the visual elements. We wanted to analyze human emotions, especially based on color, among these visual elements. For this purpose, machine learning models were established according to color and human emotion changes were analyzed quantitatively over time. Finally, we analyzed how much color affects human emotions. © 2020 IEEE.

关键词： Color

A Survey on Explainable Artificial Intelligence for Cybersecurity

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

A Prediction of Grain Yield Based on Hybrid Intelligent Algorithm 5

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A Prediction of Grain Yield Based on Hybrid Intelligent Algo...

5th International Scientific Conference of Al-Khwarizmi Society, FISCAS 2020

作者： Saleh, Ibrahim Ahmed Alawsi, Wasan Abdallah Alsaif, Omar Ibrahim Alsaif, Khalil University of Mosul Software Engineering Dept. Iraq University of Al- Qadisiyah College Science Iraq Northern Technical University Mosul Technical Institute Iraq University of Mosul Computer Science Dept. Iraq

The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013. These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set. The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it's very efficient and practicable. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting