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Classifying extended,localized and critical states in quasiperiodic lattices via unsupervised learning

Chinese physics B 2025年第1期34卷 422-427页

作者： Bohan Zheng Siyu Zhu Xingping Zhou Tong Liu School of Computer Science and Technology School of SoftwareNanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Department of Applied Physics School of ScienceNanjing University of Posts and TelecommunicationsNanjing 210003China

Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.

关键词： quantum phase quasiperiodic machine learning

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Detachable Robot That Moves the Baby Bouncer

Detachable Robot That Moves the Baby Bouncer

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Onosato, Natsumi Sawada, Naoharu Kawasaki, Yohei Takeda, Masahiko Inoue, Masaki Sugiura, Yuta Keio University Department of Information and Computer Science Kanagawa Japan Keio University Department of Applied Physics and Physico-Informatics Kanagawa Japan

ISBN: (纸本)9798331531614

A baby bouncer is a type of cradle that allows for rocking and soothing through the baby's own movements or the use of hands by an adult. An electric baby bouncer is designed to automatically rock the baby, enabling parents to soothe their child without using their hands and reducing the effort required. However, existing electric baby bouncers have the drawback of being expensive, heavy, and bulky. In this study, we propose an external attachment device that can be easily added to existing manual baby bouncers to enable automatic rocking. This device operates by transmitting the rotation of a servo motor to the baby bouncer, facilitating the rocking motion. Through experimentation, we validated that the device can adapt to variations in the baby's weight and changes in the servo motor's rotation cycle, thereby allowing for adjustments in the swing range of the baby bouncer. © 2025 IEEE.

关键词： Servomotors

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Tool for Game Plot Line Visualization for Designers, Testers and Players 1

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20th International Conference on Cooperative Design, Visualization and Engineering, CDVE 2023

作者： Nowak, Leszek Grabska-Gradzińska, Iwona Palacz, Wojciech Grabska, Ewa Guzik, Maria Department of Information Technologies Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Games Technology Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Design and Computer Graphics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (数字)9783031438158

ISBN: (纸本)9783031438141

This paper presents a tool for automatic and interactive visualization of game plots, which can be used to check whether the designers’ work meets the constraints of the world, to help testers control played game story paths. The referential designers’ game plots can be imported from any flowchart diagrams as an XML file and the actual gameplay variant is generated on the fly and applied to the original design based on detected common diagram points. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visualization

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Video classification for efficient data storage using deep learning: a comparison of sequential and simultaneous feature extraction methods

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Multimedia Tools and Applications 2025年第6期84卷 3071-3094页

作者： Dang Thi, Phuc Nguyen Dang, Hoang Thi Doan Huu, Phuc Dau Sy, Hieu Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City 12 Nguyen Van Bao Ho Chi Minh City7000 Viet Nam Department of Applied Physics Faculty of Applied Science University of Technology - Viet Nam National University HCMC To Hien Thanh Ho Chi Minh City7000 Viet Nam

Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show t

关键词： Deep learning

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and information Organization). All rights

关键词： Ant colony optimization

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Step Size Control in Evolutionary Algorithms for Neural Architecture Search 16th

Step Size Control in Evolutionary Algorithms for Neural Arch...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Nieber, Christian Mota Dias, Douglas Naredo Garcia, Enrique Ryan, Conor Department of Computer Science and Information Systems University of Limerick Limerick Ireland Department of Computer Science & Applied Physics Atlantic Technological University Galway Ireland Departamento de Ciencias Básicas Universidad del Caribe Cancun Mexico

ISBN: (纸本)9789897587214

This work examines how evolutionary Neural Architecture Search (NAS) algorithms can be improved by controlling the step size of the mutation of numerical parameters. The proposed NAS algorithms are based on F-DENSER, a variation of Dynamic Structured Grammatical Evolution (DSGE). Overall, a (1+5) Evolutionary Strategy is used. Two methods of controlling the step size of mutations of numeric values are compared to Random Search and F-DENSER: Decay of the step size over time and adaptive step size for mutations. The search for lightweight, LeNet-like CNN architectures for MNIST classification is used as a benchmark, optimizing for both accuracy and small architectures. An architecture is described by about 30 evolvable parameters. Experiments show that with step size control, convergence is faster, better performing neural architectures are found on average, and with lower variance. The smallest architecture found during the experiments reached an accuracy of 98.8% on MNIST with only 5,450 free parameters, compared to the 62,158 parameters of LeNet-5. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Evolutionary Algorithms LeNet Lightweight CNNs MNIST Neural Architecture Search Step Size Control

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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The effect of the cyclic curricular design of modelling-based instruction with virtual labs

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International Journal of science Education 2024年第1期46卷 46-72页

作者： Chang, Hsin-Yi Liu, Chen-Chung Wen, Chi-Ting Chang, Ming-Hua Fan Chiang, Shih-Hsun Hwang, Fu-Kwun Program of Learning Sciences and Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Information Management Soochow University Taipei Taiwan Department of Physics National Taiwan Normal University Taipei Taiwan

One issue facing teachers who implement model-based instruction within the school system is the tight curricular schedule. The ‘lesson objective tension’ calls for investigations to address instructional issues such as how to organise modelling-based instruction at the curriculum level which is across multiple units. We investigated the effect of the cyclic curricular design spacing out three modelling-based instruction units with virtual labs over three semesters, each involving 73, 181, and 150 junior high school students, respectively. Results indicate that, overall, the approach of modelling with virtual labs better promoted students’ scientific literacy than the traditional instruction did. Specifically, students with low and moderate school science achievements benefitted more as shown in their scientific literacy scores than did the high achievement students. Furthermore, this study found that receiving one unit of modelling-based instruction may not be sufficient to promote students’ scientific literacy, whereas the cyclic approach that suggests students receive two or more units of modelling-based instruction with virtual labs spaced over consecutive semesters had significant effects on promoting students’ scientific literacy. The results provide insights into when and how modelling-based learning with virtual labs can promote scientific literacy at a curricular level. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： lesson objective tension Modelling virtual labs

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Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

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Neural Computing and Applications 2024年第21期36卷 13357-13372页

作者： Massaoudi, Ayman Echouchene, Fraj Ben Ayed, Mossaad Berguiga, Abdelwahed Harchay, Ahlem Al-Ghamdi, Sara Belmabrouk, Hafedh Department of Computer Science College of Science and Arts in Gurayat Jouf University Sakakah Saudi Arabia Olive Research Center Jouf University Sakakah Saudi Arabia Laboratory of Electronics and Microelectronics Faculty of Science of Monastir University of Monastir Environment Boulevard Monastir5019 Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Department of Industrial Electronics ENISo University of Sousse Sousse Tunisia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Physics College of Science Majmaah University Al Majma’ah11952 Saudi Arabia

Using experimental results related to the biosorption of Fe(III) by activated carbon derived from olive pit waste, we developed and evaluated four artificial neural network (ANN) models in this study, namely MLP-ANN, RBF-ANN, GR-ANN, and PSO-ANN, to predict the removal efficiency of Fe(III) during the adsorption process. The purpose of these models was to forecast the effect of the following five important operational variables: the initial concentration, time, stirring speed, temperature, and biosorbent dose. We conducted a thorough assessment of the performance of these models and compared their ability to predict the removal capacity. Several statistical metrics have been used to quantify the quality of the different models. The results calculated by the machine learning models were analyzed and compared with the experimental results. The obtained values of the coefficient of determination were 0.9997 for the PSO-ANN model, 0.991 for the GR-ANN model, 0.983 for the RBF-ANN model, and 0.837 for the MLP-ANN model concerning the removal efficiency. All the studied models are able to accurately predict the adsorbed quantity. ANOVA analysis was used to evaluate the effect of the parameters inherent to the PSO-ANN model. The PSO-ANN model proves to be a powerful tool for estimating the efficiency of Fe(III) ion removal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Biosorption

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On the Characterization of Quantum Flip Stars with Quantum Network Tomography 4

On the Characterization of Quantum Flip Stars with Quantum N...

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： De Andrade, Matheus Guedes Navas, Jake Montaño, Inès Towsley, Don Manning College of Information and Computer Science University of Massachusetts Amherst United States Northern Arizona University Department of Applied Physics and Materials Science United States

ISBN: (纸本)9798350343236

The experimental realization of quantum information systems will be difficult due to how sensitive quantum information is to noise. Overcoming this sensitivity is central to designing quantum networks capable of transmitting quantum information reliably over large distances. Moreover, the ability to characterize communication noise in quantum networks is crucial in developing network protocols capable of overcoming the effects of noise in quantum networks. In this context, quantum network tomography refers to the characterization of channel noise in a quantum network through end-to-end measurements. In this work, we propose network tomography protocols for quantum star networks formed by quantum channels characterized by a single, non-trivial Pauli operator. Our results further the end-to-end characterization of quantum bit-flip star networks by introducing tomography protocols where state distribution and measurements are designed separately. We build upon previously defined quantum network tomography protocols, as well as provide novel methods for the unique characterization of bit-flip probabilities in stars. We introduce a theoretical benchmark based on the Quantum Fisher information matrix to compare the efficiency of quantum network protocols. We apply our techniques to the protocols proposed, and perform an initial analysis on the potential benefits of entanglement for Quantum Network Tomography. Furthermore, we simulate the protocols using N etSquid to assess the convergence properties of the estimators obtained for particular parameter regimes. Our findings show that the efficiency of protocols depend on parameter values and motivate the search for adaptive quantum network tomography protocols. © 2023 IEEE.

关键词： Parameter estimation

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