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Optimal scheduling of graph states via path decompositions

Physical Review A 2025年第1期111卷 012627-012627页

作者： Samuel J. Elman Jason Gavriel Ryan L. Mann Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Centre for Quantum Computation and Communication Technology Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia

We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path decomposition of minimal width. Our analysis shows that approximating the spatial cost of a graph is NP-hard, while for graphs with bounded spatial cost, we establish an efficient algorithm for computing an optimal measurement schedule.

关键词： Quantum computers

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Design and Implementation of Intelligent Dustbin with Garbage Gas Detection for Hygienic Environment based on IoT

Design and Implementation of Intelligent Dustbin with Garbag...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Ahmed, Marzia Shaha, Rony Sarker, Kaushik Mahi, Rifat Bin Kashem, Mohammod Abul Daffodil International University Department of Software Engineering Dhaka Bangladesh Universiti Malaysia Pahang Faculty of Electrical and Electronics Engineering Pahang 26600 Malaysia Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Duet Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Rapid population expansion necessitated increased resource use in everyday living. As a result, the pace of trash gen-eration has increased dramatically, affecting the environment's hygiene system and other health concerns. Waste overflows in public spaces, and improved management is necessary. The purpose of this study is to develop a model of an intelligent trashcan for usage in smart cities. Additionally, to identify dangerous gases emitted by dustbins for subsequent management operations, as well as to monitor the amount of trash in the waste bin and warn the municipality through SMS. This system includes two ultrasonic sonar sensors for measuring trash level, a GSM module for sending SMS, three gas sensors for detecting harmful garbage gas, an infrared sensor for counting garbage droplets, and an Arduino Uno for managing all activities. The system notifies you whether the bin is full or empty and can also be controlled by voice command. Additionally, released gas may be monitored to determine the severity of the impairment and to notify the appropriate authorities. Most significantly, it will identify a failed trash drop in the bin and alert the user through alarm for truly considering the reduction of spilled garbage surrounding bins while using the system. © 2022 IEEE.

关键词： Global system for mobile communications

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PASTD: Progressive Augmentation and Spatiotemporal Decoupling Contrastive Learning for Skeleton-Based Action Recognition

PASTD: Progressive Augmentation and Spatiotemporal Decouplin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qian Huang Weiwen Qian Chang Li Gongyou Xu Zhongqi Chen College of Computer Science and Software Engineering Hohai University Nanjing China Faculty of Science and Technology United International College BNU-HKBU Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Contrastive learning has achieved significant progress in the field of self-supervised skeleton-based action recognition. However, existing methods often apply strong augmentations directly to skeleton data, which can distort or even lose the semantic of the skeletons. Additionally, most methods focus on unified feature extraction through spatiotemporal modeling, leading to spatiotemporal entanglement that hinders model’s interpretability and degrades performance. To address these issues, we propose a Progressive Augmentation and SpatioTemporal Decoupling contrastive learning model (PASTD). Specifically, we propose progressive augmentation to enhance the model’s generalization capability, which generates multiple distinct positive sample pairs through multiple branches and uses inter-branch constraints to maintain consistent motion patterns. Moreover, we propose spatiotemporal decoupling module to separate the feature’s spatial and temporal information, using a dual-path self-attention module combined with an intra-branch cross-domain learning strategy to facilitate information exchange between domains. Extensive experiments on the four public datasets NTU-RGB+D (60&120) and PKU-MMD (I&II) demonstrate the effectiveness of these components. Moreover, PASTD outperforms state-of-the-art methods across various evaluation metrics.

关键词： Measurement Three-dimensional displays Semantics Contrastive learning Speech recognition Signal processing Skeleton Spatiotemporal phenomena Speech processing Information exchange

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview 16

Automated Recognition of Structures in Scanning Electron Mic...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Marascu, Valentina Mihailescu, Marius Iulian Nita, Stefania Loredana Rogobete, Marius 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania 'Ferdinand i' Military Technical Academy Department of Computers and Cybersecurity Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. Informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified. © 2024 IEEE.

关键词： Python

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The Impact Of Social Media On The Academic Development Of School Students 10

The Impact Of Social Media On The Academic Development Of Sc...

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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Perera, U.D.Harshi Lahiruka Harshanath, S.M.B. Sri Lanka Institute of Information Technology Faculty of Graduate Studies Research Sri Lanka Sliit Department of Computer Science and Software Engineering Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

While social media has reached the peak of its popularity, the addiction to social media become a major issue. With the present pandemic the authorities are forced to close the schools and to shift to virtual classrooms. The students must therefore use a connected devise to participate to the classes which has contributed to the increased addiction to social media among the youth. Inappropriate or excessive usage social media may affect the studies as well as mental and physical health of the user. Spread of fake news through social media has also been a big problem. Mainly, due to its convenience of usage, social media, has become a daily necessity, especially among the youth. So, the primary goal of this study project is to determine the impact of social media on school students' academic performance and to suggest a separate social media platform which will meet their needs. © 2022 IEEE.

关键词： Students

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Model-Based Testing of Access Control Requirement in Multi-tenant Application: An Extensive Life Cycle 1

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3rd International Conference on computer science, Electrical and Electronic engineering, ICCEE 2021

作者： Duraisamy, Gunavathi Ghani, Abdul Azim Abd Zulzalil, Hazura Abdullah, Azizol Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Selangor Serdang Malaysia Faculty of Computer Science and Information Technology Department of Communication Technology and Networking Universiti Putra Malaysia Selangor Serdang Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Sungai Long Campus Selangor Kajang Malaysia

ISBN: (数字)9789811697814

ISBN: (纸本)9789811697807

Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity of model-based testing is the modelling activity. Models are used to explain and represent the behaviour of the product. The requirement will be translated into models and those models will be used in generating the test cases automatically in model-based testing. The modelling activity clarifies test requirement and contribute to automatic generation and execution of large test suites with tool support. Model-based testing provides numerous advantages in testing;mainly on achieving appropriate test coverage and reduce manual effort and time spent since it can execute the test cases automatically. Despite all the benefit it can provide, there are some drawbacks too. However, the challenges faced can overcome by implementing proper test modelling activity and by adopting a well-versed test management activity. Thus, in this paper, we have studied the existing testing process, the taxonomy of model-based testing and model-based testing steps and proposed an extensive lifecycle for model-based testing with detail activities and steps to be carried out in order to test and validate access control requirement for multi-tenant application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection

Bridging Machine Learning and Formal Concept Analysis for Ef...

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Information Technology (ACIT)

作者： Anas M. Al-Oraiqat Oleksandr Drieiev Ghassan Samara Sattam Almatarneh Karim A. Al-Oraiqat Hazim Alrawashdeh Hanna Drieieva Ali Elrashidi Yassin M. Y. Hasan Department of Computer Science Faculty of Information Technology Zarqa University Jordan Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Data Science and Artificial Intelligence Faculty of Information Technology Zarqa University Jordan Department of Software Engineering Faculty of Information Technology Zarqa University Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Electrical Engineering Department Assiut University Egypt

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude" low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering" of crowded zones into hierarchical formal concepts and these latter’s "bottom-up parsing" to finally identify the dense zones.

关键词： Formal concept analysis Scalability Prevention and mitigation Decision making Lattices Machine learning Feature extraction Prediction algorithms Real-time systems Data mining

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Ethics of AI: A Systematic Literature Review of Principles and Challenges 22

Ethics of AI: A Systematic Literature Review of Principles a...

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26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Khan, Arif Ali Badshah, Sher Liang, Peng Waseem, Muhammad Khan, Bilal Ahmad, Aakash Fahmideh, Mahdi Niazi, Mahmood Akbar, Muhammad Azeem M3S Empirical Software Engineering Research Unit University of Oulu Finland Faculty of Computer Science Dalhousie University Canada School of Computer Science Wuhan University China Department of Computer Science University of Loralai Pakistan College of Computer Science and Engineering University of Hail Saudi Arabia School of Business University of Southern Queensland Australia Department of Information and Computer Science King Fahd University of Petroleum and Minerals Finland Software Engineering Department Lappeenranta-Lahti University of Technology Finland

ISBN: (纸本)9781450396134

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements. © 2022 ACM.

关键词： Challenges AI Ethics Machine Ethics Systematic Literature Review Principles

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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Multimodal Data Fusion Based on Generative Adversarial Network in University Teaching Evaluation

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 845-858页

作者： Liu, Min Teng, Hua Zhang, Hong Li, Hao School of Computer Science and Software Engineering Southwest Petroleum University Nanchong637001 China Department of Computer China West Normal University Nanchong637009 China Faculty of Computer and Information Science SouthWest University Chongqing400700 China Krirk University Bangkok10220 Thailand

With the advancement of educational evaluation reform and the popularization of higher education, university teaching quality evaluation has been a research hotspot in the field of educational evaluation. In response to the issue of multimodal heterogene-ity gap in current university education evaluation data, which makes it tough to integrate evaluation data, this article suggests a Multimodal Data Fusion (MDF) method for university teaching evaluation relied on Generative Adversarial Network (GAN). Firstly, the MDF model is constructed from two aspects: establishing similarity network and designing regularization terms. By designing sparse regularization terms, the feature selection of multimodal samples is carried out to address the issue of large deviation of fusion data. Secondly, BERT pre-training model is adopted to extract the modal embedding features of teaching evaluation text, and Bi-LSTM is adopted to extract the audio and visual modal embedding features of teaching evaluation. Then, on the ground of the constructed MDF model, combined with the GAN and autoencoder, a multi-modal adversarial autoencoder learning model is established to reduce the heterogeneous gap between modes and form a unified representation. At last, the self-attention pretest network is established to achieve the automatic classification of multi-modal university teaching evaluation data. The experimental outcome implies that the accuracy, F1 value and determination coefficient R2 of the suggested method are 92.18%, 93.72% and 90.5%, respectively, which are superior to the comparison method, showing good classification performance. © 2025, J. Network Intell. All rights reserved.

关键词： Image fusion

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