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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

引用

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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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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International Research Conference on Smart Computing and systems Engineering (SCSE)

作者： Isuru Kavinda Harinda Fernando Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors’ use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain.

关键词： Training Handwriting recognition Recurrent neural networks Convolution Computational modeling Transfer learning Medical services

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Meta-Heuristic Based Multipath Joint Routing and Scheduling of Time-Triggered Traffic for Time-Sensitive Networking in IIoT 2

Meta-Heuristic Based Multipath Joint Routing and Scheduling ...

引用

2nd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2022

作者： Chen, Chen Li, Zhong The College of Information Science and Technology Donghua University Shanghai201620 China The Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (纸本)9781510662940

Time-Sensitive networking (TSN) meets the needs of industrial internet of things (IIoT). It solves the challenges of deterministic transmission and reliable communication of time sensitive data streams. Traffic scheduling is the core mechanism of time-sensitive networks. Many excellent researches have explored and optimized the method of scheduling time-triggered streams in TSN. However, the existing time-triggered streams scheduling in TSN mostly separates routing and scheduling, which limits the scalability of scheduling. Many researches are based on fixed routing to schedule streams. Due to the interaction between scheduling and routing, the quality of fixed route solution is inferior to that of joint routing and scheduling. In this paper, we propose a meta-heuristic based multipath joint routing and scheduling method for time-triggered traffic in TSN, named MMRS. We set path selection as a variable and consider multipath routing for fault tolerance. At the same time, we establish the integer linear programming formulation and use meta-heuristic to obtain high-quality solutions. The evaluations show that compared with other excellent routing and scheduling methods, the runtime of 60 streams of MMRS method is reduced by 65.6%, and the schedulability is improved by 21% on average. The experiments verify that our proposed scheduling method can obtain high-quality solutions in an acceptable solution time. © 2023 SPIE.

关键词： Internet of things

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Model for Assessment information Security Awareness Level for Data Stewardship by Understanding the Context of Use 2022

Model for Assessment Information Security Awareness Level fo...

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11th International Conference on Software and computer Applications, ICSCA 2022

作者： Lubis, Muharman Azies Muslim, Abdul Ridho Lubis, Arif Alkhowarizmi, Alkhowarizmi Department of Information System Telkom University Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Indonesia Department of Information Technology Universitas Muhammadiyah Sumatera Utara Indonesia

ISBN: (纸本)9781450385770

The role of data stewardship satisfies various requirement for the users, employee or staff to provide necessary awareness in ensuring information security strategy take place accordingly. It relates to have properly understand the definition and standard across the organization in term of quality, accuracy, workflows, usage, compliance, format and attribute of content and metadata. Unfortunately, many organization have neglected the importance of data stewardship as the assistance for leveraging the valuable asset of data at the optimization capacity. One of advantages that can be generated automatically related to improve the awareness of relevant party within the environment in protecting the information assets based on policy implemented. This study identifies the relationship by accessing data stewardship with the awareness campaign strategy through security awareness domain and resources (SADAR) framework. The study compare between the utilization in the banking industry by using enterprise resource planning (ERP) and in the education with open platform of database management system (DBMS). © 2022 ACM.

关键词： Data Stewardship Security Awareness Assessment

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Robotic Anchoring system with Two Locomotion Modes for Preoperative Localization of Colorectal Cancer

Robotic Anchoring System with Two Locomotion Modes for Preop...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wang, Jiale Ren, Hao Yang, Liu Zhao, Haoxiang Zhang, Ruijia Shen, Yajing Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong City University of Hong Kong Department of Biomedical Engineering Kowloon Hong Kong Institute of Robotics and Automatic Information System Nankai University China

ISBN: (纸本)9781665481090

At present, colorectal cancer is one of the most prevalent cancers in the world. Colectomy serves as an effective treatment option for this disease. Preoperative localization plays a vital role in the colectomy procedure. However, traditional preoperative localization techniques have a greater risk of infection and lower accuracy. Moreover, it is difficult for the previous anchoring robots to adapt to the unstructured environment of the GI tract because of their limited movement abilities. To address these challenges, this work develops a robotic anchoring system with two locomotion modes which can anchor in the designated position. Driven by the external magnetic field, the robot can achieve slipping and flipping in the stomach. When the gradients of the field increase, the robot is triggered to deploy the anchoring mechanism in the intestine. Two in vitro experiments are conducted in the stomach and intestine phantoms. The first experiment demonstrates that the robot system can achieve both slipping and flipping in the stomach and its average position error is 2.96mm. The second experiment shows that the robot system can anchor in the designated position near the tumor and can resist a peristalsis force of 0.82N from the intestine wall. © 2024 IEEE.

关键词： Diseases

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Cascaded Diffusion Models for Virtual Try-On: Improving Control and Resolution 39

Cascaded Diffusion Models for Virtual Try-On: Improving Cont...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Guangyuan Wang, Yongkang Luan, Junsheng Zhao, Lei Xing, Wei Lin, Huaizhong Ou, Binkai College of Computer Science and Technology Zhejiang University China Innovation Research & Development BoardWare Information System Limited Macao

ISBN: (纸本)157735897X

Previous virtual try-on methods have employed ControlNet architecture in exemplar-based inpainting diffusion models to guide the generation of try-on images, preserving the garment’s features and enhancing the realism of the generated images. While these methods have maintained the identity of the garment and improved the naturalness of the generated images, they still face the following limitations: (1) For garments with complex features, such as intricate text, patterns, and uncommon styles, they struggle to retain these detailed features in the generated try-on images. (2) They are limited to generating try-on images at a maximum resolution of 1K, which may not meet the demands of real-world scenarios, where higher resolutions might be required. To address the aforementioned issues, in this paper, we propose a Cascaded Diffusion Model for virtual try-on to enhance both image controllability and resolution. We call it CDM-VTON. Specifically, we design two diffusion models: the Multi-Conditioned Diffusion Model (MC-DM) and the Super-Resolution Diffusion Model (SR-DM). The former generates low-resolution try-on images while preserving the garment’s complex features, and the latter enhances the resolution of these images. Additionally, we incorporate a multi-control integration module in the MC-DM, which injects multiple control conditions into a frozen denoising U-Net to ensure that the generated try-on images retain complex garment features. Our experimental results demonstrate that our method outperforms previous approaches in preserving garment details and generating authentic virtual try-on images, both qualitatively and quantitatively. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Digital arithmetic

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Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification

Preliminary Study on the Effect of Traffic Representation on...

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2022 IEEE International Conference on Computing, ICOCO 2022

作者： Aqil, Nik Afifi, Firdaus Zaki, Faiz Anuar, Nor Badrul Faculty of Computer Science and Information Technology Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu21030 Malaysia

ISBN: (纸本)9781665489966

The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset. © 2022 IEEE.

关键词： Internet of things

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BMSAD: Behavior Feature and Multi-Scenario -Based Sybil Attack Detection Method in Vehicular Networks 3

BMSAD: Behavior Feature and Multi-Scenario -Based Sybil Atta...

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3rd International Seminar on Artificial Intelligence, Networking, and information technology, AINIT 2022

作者： Luo, Jie Li, Zhong College of Information Science and Technology Donghua University Shanghai201620 China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

Vehicular networks improve traffic safety and efficiency by wireless communications among vehicles and infrastructures. However, security has always been a challenge to vehicular networks, which may cause severe harm to the intelligent transportation systems. Sybil attack is considered as a serious security threat to vehicular networks since the attacker can disseminate false messages with multiple forged identities. In this paper, we designed a behavior feature and multi-scenario-based Sybil attack detection method, named BMSAD in vehicular networks. In BMSAD, we propose solutions for two scenarios: normal traffic and traffic congestion. A node is allowed to verify the authenticity of another node by estimating their geographic distance based on received signal strength, and compare them to its claimed localizations. Then we design long- and short-term pattern to analyze the similarity of vehicles' behavior features and trajectory for Sybil nodes detection, which act as the second line of defense in normal traffic scenario. At last, the Sybil nodes of the traffic congestion scenario are detected by logical inference based on kinematics theory. And experiment results demonstrate that the proposed scheme achieves high detection rate and low false positive rate. © 2023 SPIE.

关键词： Traffic congestion

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F-CNN: Faster CNN Exploiting Data Re-Use with Statistical Analysis 5

F-CNN: Faster CNN Exploiting Data Re-Use with Statistical An...

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5th IEEE International Conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Alantali, Fatmah Halawani, Yasmin Mohammad, Baker Al-Qutayri, Mahmoud Khalifa University System-on-Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates University of Dubai College of Engineering & Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350332674

Many of the current edge computing devices need efficient implementation of Artificial Intelligence (AI) applications due to strict latency, security and power requirements. Nonetheless, such devices, face various challenges when executing AI applications due to their limited computing and energy resources. In particular, Convolutional Neural Networks (CNN) is a popular machine learning method that derives a high-level function from being trained on various visual input examples. This paper contributes to enabling the use of CNN on resource-constrained devices offline, where a trade-off between accuracy, running time and power efficiency is verified. The paper investigates the use of minimum pre-processing methods of input data to identify nonessential computations in the convolutional layers. In this work, Spatial locality of input data is considered along with an efficient pre-processing method to mitigate the accuracy loss caused by the computational re-use approach. This technique was tested on LeNet and CIFAR-10 structures and was responsible for 1.9% and 1.6% accuracy loss while reducing the processing time by 38.3% and 20.9% and reducing the energy by 38.3%, and 20.7%, respectively. The models were deployed and verified on Raspberry Pi 4 B platform using the MATLAB coder to measure time and energy. © 2023 IEEE.

关键词： Convolution

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Social Engineering Attacks Detection Approach 13

Social Engineering Attacks Detection Approach

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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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