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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

arXiv

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arXiv 2024年

作者： Hongo, Wataru Ando, Shuji Tsuchida, Jun Sozu, Takashi Department of Information and Computer Technology Graduate School of Engineering Tokyo University of Science Tokyo Japan Advanced Quantitative Sciences Development Novartis Pharma K.K. Tokyo Japan Department of Information Sciences Faculty of Science and Technology Tokyo University of Science Chiba Japan Department of Data Science Faculty of Data Science Kyoto Women’s University Kyoto Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan

Background: When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using outcome-adaptive lasso was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the outcome-adaptive lasso to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the outcome-adaptive lasso for PS models and various outcome-oriented covariate selection via penalization for outcome models. Methods: We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. In numerical experiments, we assessed bias, standard error, and root-mean-squared error. We applied the AIPW estimators to a clinical trial dataset for reference, comparing their point estimates and standard errors obtained via bootstrap. Results: In the numerical experiments, the performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was

关键词： Mean square error

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DCSim: Computing and Networking Integration based Container Scheduling Simulator for data Centers

arXiv

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arXiv 2024年

作者： Hu, Jinlong Rao, Zhizhe Liu, Xingchen Deng, Lihao Dong, Shoubin Guangdong Key Lab of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou China

The increasing prevalence of cloud-native technologies, particularly containers, has led to the widespread adoption of containerized deployments in data centers. The advancement of deep neural network models has increased the demand for container-based distributed model training and inference, where frequent data transmission among nodes has emerged as a significant performance bottleneck. However, traditional container scheduling simulators often overlook the influence of network modeling on the efficiency of container scheduling, primarily concentrating on modeling computational resources. In this paper, we focus on a container scheduling simulator based on collaboration between computing and networking within data centers. We propose a new container scheduling simulator for data centers, named DCSim. The simulator consists of several modules: a data center module, a network simulation module, a container scheduling module, a discrete event-driven module, and a data collection and analysis module. Together, these modules provide heterogeneous computing power modeling and dynamic network simulation capabilities. We design a discrete event model using SimPy to represent various aspects of container processing, including container requests, scheduling, execution, pauses, communication, migration, and termination within data centers. Among these, lightweight virtualization technology based on Mininet is employed to construct a software-defined network. An experimental environment for container scheduling simulation was established, and functional and performance tests were conducted on the simulator to validate its scheduling simulation capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Software testing

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Research on Frame and Table Recognition Based on CAD Drawings

Research on Frame and Table Recognition Based on CAD Drawing...

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Chinese Automation Congress (CAC)

作者： Hui Zhong Yuanman Miao Liaoning Province Big Data Management and Analysisline Laboratory of Urban Construction School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the process of drawing architectural drawings with AutoCAD software, enterprises will produce a large number of CAD drawings in DWG format. The tables of these CAD drawings contain rich textual information. These text information constitute the key data base of enterprise systematic management drawings. At present, the systematic management of drawing data needs to collect these sheet data, but the sheet data collection mainly depends on manual input, which leads to high error rate of data collection, huge workload, long compilation cycle and low work efficiency. The purpose of this paper is to discuss how to deal with these tables efficiently. Firstly, based on Revit secondary development, the frame recognition algorithm is proposed, which confirms the inner and outer frame by finding the largest rectangle and the second largest rectangle. In addition, the Teigha class library is used to extract text, so as to extract key data, so as to promote the collection, reuse and systematic management of product data. Through the processing of drawing frames and forms, it lays a good foundation for subsequent component identification. The method proposed in this paper has strong versatility and adaptability, and can basically realize the recognition of CAD drawing frame and form, and extract form text, and achieve higher accuracy and efficiency.

关键词： Systematics Accuracy Text recognition Fault tolerant systems Manuals data collection Libraries Software data mining Optimization

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A Method of Advanced Persistent Threat data Processing Based on Ensemble Learning

A Method of Advanced Persistent Threat Data Processing Based...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xuelong Wang Xin Zheng Yang Tu School of Computer Science Xi'an Shiyou University Xi'an Shaanxi China Internet data center Xi'an Shiyou University Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Finding the features with the highest correlation degree in the source APT data from network security devices is essential for preprocessing the data in order to train models that will increase the accuracy of APT attack classification. The preprocessing, cleaning, and feature transformation of the dataset are the first steps in the application of data mining techniques in this work. VarianceThreshold is ultimately chosen for feature selection to create the optimal feature subset after a range of feature selection techniques are used for experimental comparison. The best feature subset is then used to compare and evaluate several machine learning algorithms, and by integrating ensemble learning methods, the VT-stacking model is recommended. The experimental results demonstrate that the VT-stacking model developed in this paper achieves 97.3% accuracy in categorizing APT assaults after preprocessing the APT data.

关键词： Accuracy Machine learning algorithms Firewalls (computing) Stacking Network security Signal processing Feature extraction data processing data models Ensemble learning

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Overcoming data Inequality across Domains with Semi-Supervised Domain Generalization

arXiv

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arXiv 2024年

作者： Park, Jinha Cho, Wonguk Kim, Taesup Department of Electrical and Computer Engineering Seoul National University Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of

While there have been considerable advancements in machine learning driven by extensive datasets, a significant disparity still persists in the availability of data across various sources and populations. This inequality across domains poses challenges in modeling for those with limited data, which can lead to profound practical and ethical concerns. In this paper, we address a representative case of data inequality problem across domains termed Semi-Supervised Domain Generalization (SSDG), in which only one domain is labeled while the rest are unlabeled. We propose a novel algorithm, ProUD, which can effectively learn domain-invariant features via domain-aware prototypes along with progressive generalization via uncertainty-adaptive mixing of labeled and unlabeled domains. Our experiments on three different benchmark datasets demonstrate the effectiveness of ProUD, outperforming all baseline models including single domain generalization and semi-supervised learning. Source code will be released upon acceptance of the paper. Copyright © 2024, The Authors. All rights reserved.

关键词： Population statistics

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EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle Detection Network in High Resolution Aerial Imagery

EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nandini Saini Shubham Gupta Chiranjoy Chattopadhyay Debasis Das Suman Kundu Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model’s final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

关键词： Visualization Accuracy Image resolution Explainable AI Error analysis Vehicle detection Object detection

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Uncovering meanings of embeddings via partial orthogonality 23

Uncovering meanings of embeddings via partial orthogonality

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yibo Jiang Bryon Aragam Victor Veitch Department of Computer Science University of Chicago Booth School of Business University of Chicago Department of Statistics University of Chicago and Data Science Institute University of Chicago

Machine learning tools often rely on embedding text as vectors of real numbers. In this paper, we study how the semantic structure of language is encoded in the algebraic structure of such embeddings. Specifically, we look at a notion of "semantic independence" capturing the idea that, e.g., "eggplant" and "tomato" are independent given "vegetable". Although such examples are intuitive, it is difficult to formalize such a notion of semantic independence. The key observation here is that any sensible formalization should obey a set of so-called independence axioms, and thus any algebraic encoding of this structure should also obey these axioms. This leads us naturally to use partial orthogonality as the relevant algebraic structure. We develop theory and methods that allow us to demonstrate that partial orthogonality does indeed capture semantic independence. Complementary to this, we also introduce the concept of independence preserving embeddings where embeddings preserve the conditional independence structures of a distribution, and we prove the existence of such embeddings and approximations to them.

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Finding customer behavior insights for content creation in material and product sourcing using specialized topic analysis 22

Finding customer behavior insights for content creation in m...

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22nd International Conference on Electronic Business, ICEB 2022

作者： Chotisarn, Noptanit Siriphongwatana, Phanuphong Thanisuwiphat, Phattranit Gulyanon, Sarun Nadee, Winai Department of Management Information Systems Thammasat Business School Thammasat University Bangkok Thailand Data Science and Innovation Program College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized information but also specific information, which can be different across markets and cultures. This information also needs some standards so it can be analyzed systematically. This paper aims to obtain customer insight into web content. Inside the web content, one possible source of this information is the tags based on customer feedback and the related entities. In this case, the product review data were collected and analyzed. However, manually analyzing feedback is a time-consuming activity. In this work, we formulated the topic analysis problem specialized for material and product sourcing, which could benefit product analysis and development. Technically, we also compared different text processing and classification methods, which set the benchmarks for reviewing the model performance in the future. © 2022 International Consortium for Electronic Business. All rights reserved.

关键词： Sales

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An Emotion Recognition Model Augmented a Small Amount of Individual EEG by Domain Adaptation

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2022年第7期142卷 771-780页

作者： Furukawa, Shoya Sakuma, Takuto Kato, Shohei Creative Engineering Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Computer Science Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Frontier Research Institute for Information Science Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan

In recent years, making computers understand the emotions of users is necessary because emotions are an important factor in human communication. Among many methods of recognizing emotions, EEG is widely used because it has high temporal resolution and it is impossible to disguise intentionally. However, it is necessary to acquire new user data and construct the personal Emotion recognition model since EEG varies widely among individuals. The conventional methods lack practicality because it builds a different model for every new user data. Our method builds a model in a single training using new user data. To reduce the number of new user data for training and to relieve the burden of EEG measurement, we adapt existing user data. We absorb the individual differences in EEG between a new user and existing users by domain adaptation using a small number of new user data and construct a model by deep neural networks. From the experiments, we confirmed that the proposed method performs as well as the conventional methods, even though it is built with single training using new user data. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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