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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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Towards Classifying File Segments in Memory Using Machine-Learning 9

Towards Classifying File Segments in Memory Using Machine-Le...

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9th International Conference on information Technology Trends, ITT 2023

作者： Al-Saleh, Mohammed I. Alkouz, Akram Alarabeyyat, Abdulsalam Bouchahma, Majed Jordan University of Science and Technology Computer Science Department Jordan Higher Colleges of Technology Computer Information System United Arab Emirates Al-Balqa Applied University Software Engineering Department Jordan-Salt Jordan

ISBN: (纸本)9798350327502

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator's time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating system's paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology. © 2023 IEEE.

关键词： Digital storage

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MvSR-NAT: Multi-view Subset Regularization for Non-Autoregressive Machine Translation

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IEEE/ACM Transactions on Audio Speech and Language Processing 2022年 1-10页

作者： Xie, Pan Li, Zexian Zhao, Zheng Liu, Jiaqi Hu, Xiaohui School of Automation Science and Electrical Engineering Beihang University Beijing China China Academy of Launch Vehicle Technology Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of SciencesorgName Beijing China

Conditional masked language models (CMLM) have shown impressive progress in non-autoregressive machine translation (NAT). They learn the conditional translation model by predicting the random masked subset in the target sentence. Based on the CMLM framework, we introduce Multi-view Subset Regularization (MvSR), a novel regularization method to improve the performance of the NAT model. Specifically, MvSR consists of two parts: (1) shared mask consistency: we forward the same target with different mask strategies, and encourage the predictions of shared mask positions to be consistent with each other. (2) model consistency, we maintain an exponential moving average of the model weights, and enforce the predictions to be consistent between the average model and the online model. Without changing the CMLM-based architecture, our approach achieves remarkable performance on three public benchmarks with 0.7-1.15 BLEU gains over previous NAT models. And, we reduce the gap to the stronger Transformer baseline. IEEE

关键词： Machine translation

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Mobile and Simulation-based Approach to reduce the Dyslexia with children Learning Disabilities 10

Mobile and Simulation-based Approach to reduce the Dyslexia ...

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

作者： Muthumal, S.A.D.M. Neranga, K.T. Harshanath, S.M.B. Sandeepa, V.D.R.P. Lihinikaduwa, D.N.R. Rajapaksha, U.U.S.K. Sri Lanka Institute of Information Technology Department of Information System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Learning disabilties are frequently overlooked while treating various limitations. The primary causes for such a dropout might be a lack of awareness of the risks and access to adequate medical care. Because Learning Disabilities are neurological illnesses, their etiology is unknown. Learning Disabilities do not have a therapy or a cure. Dyslexia, Dysgraphia and Dyscalculia are the most common types of Learning Dis-abilities among school children. There are numerous approaches for diagnosing and treating this ailment available today. The optimal method is to make use of a mobile application. Existing applications either do not adequately handle the problem or have minor limitations in terms of meeting genuine expectations. We can mitigate this dysfunction by utilizing mobile and simulation-based technologies. Furthermore, earlier research has not given contact and curiosity among children a significant emphasis and children have not interacted with robots. Therefore, this paper, introduces interactive and collaborative mobile appli-cation called 'Helply' with a robotic based simulation that may foster learning and help children improve and encour-age Color identification skills, Reading skills and Short-term memory skills the learning process while reducing their other dyslexic disorders. Also, NAO Robot is used to taking inputs as voice clip using voice recognition technology and images using image processing technology which embedded in NAO robot. We constructed three models for three distinct disorders and obtained accurate findings. CNN model for colour disorder had a training accuracy of 93%, FFNN model for short-term memory disorder had a training accuracy of 98%, and CNN model for reading disorder had a training accuracy of 94%. © 2022 IEEE.

关键词： Diagnosis

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of Computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Research on the Remaining Useful Life Prediction Model of Complex Equipment 4

Research on the Remaining Useful Life Prediction Model of Co...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Tang, Lei Tang, Yifei Zhang, Junhao Liu, Mengjuan Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331541729

With the rapid advancement of artificial intelligence technology, the development of intelligent manufacturing has become an inevitable trend. Utilizing AI technology to ensure the safe operation of factories is a crucial aspect of smart factories. In particular, research focused on fault diagnosis and remaining useful life (RUL) prediction models based on machine learning has garnered significant attention from both academia and industry. This paper explores RUL prediction models for complex equipment, introduces four representative models, and analyzes their performance using two benchmark datasets, C-MAPSS and N-CMAPSS. Finally, the paper discusses the future research and application trends of artificial intelligence technology in complex equipment operation and maintenance. © 2024 IEEE.

关键词： Smart manufacturing

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Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease 1

Implementation Learning Vector Quantization Using Neural Net...

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1st Lekantara Annual Conference on engineering and information Technology, LiTE 2021

作者： Samsir, Samsir Daulay, Nelly Khairani Harahap, Syaiful Zuhri Zalmi, Wahyuni Fithratul Sari, Afni Nia Nasution, Fitri Aini Watrianthos, Ronal Department of Informatics Engineering Universitas Al Washliyah Labuhanbatu Indonesia Department of Computer System Universitas Bina Insan Indonesia Department of Information System Universitas Labuhanbatu Indonesia Department of Information System Universitas Putra Indonesia Padang Indonesia Department of Software Engineer Universitas Dharmawangsa Medan Indonesia

This study aims to use data from 57 patients at Rantauprapat Hospital to train a Neural Network using a quantization learning vector method for the categorization of ear, nose, and throat disorders. The input factors were fever, tiredness, nausea, breathing pain, sore throat, hearing loss, allergies, chills and sweating, and thick and transparent mucus. The factors studied were ear canal infections, pharyngitis of the neck, throat, nose, and sinusitis. The findings revealed that ten neurons with an objective value of 0.01 in the learning rate range of 0.01 - 0.05 resulted in categorizing snoring, nose, and ear disorders, including the input layer. The MATLAB program is utilized in this approach, with an average accuracy of 67 per cent and a mean square error of 0.2. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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A Novel Method for Estimate the Structure Design Complication of Multi-Agents Based systems 2

A Novel Method for Estimate the Structure Design Complicatio...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2022

作者： Elmarzaki, Howayda A. Abdelaziz, Tawfig M. Elakeili, Salwa Maatuk, Abdelsalam University of Benghazi Dept. Software Engineering Department Benghazi Libya Libyan International Medical University Dept. Information System Department Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Making a design decision in architecture design (AD) is challenging, especially when the system has many components and interactions. The complication is evident in the design phase of agents-based systems that are assigned various responsibilities, according to most works of literature. The study's purpose is to assist system designers in developing effective architectures for agents systems that are not complicated and easy to understand. Complex Task Measurement is a novel estimation of entanglement between agents' tasks presented in this paper (CTM). This method is built on the use-case point perspective, however with several adjustments to meet the demands of agents. Before making a design decision, this innovative measurement can be utilized to estimate the architecture design. The findings will be put to use in a variety of situations. As a case study, the research will be applied to the book recommendations system, and the results will be presented after the method has been implemented. © 2022 IEEE.

关键词： Design

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