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Congress on Smart Computing Technologies, CSCT 2023

作者： Sobha Xavier, P. Sathish, P.K. Raju, G. Department of Computer Science and Engineering School of Engineering and Technology CHRIST University Bengaluru India

ISBN: (纸本)9789819750801

This study explores the adaptability of a segmentation model, originally trained on pre-operative MRI data, in post-operative recurrent brain tumor segmentation. We utilized the Attention U-Net model for this study. In pre-operative training, the model achieved a Dice Coefficient of 0.92 and an IOU of 0.86 for brain tumor MRI segmentation. Due to the surgical artifacts in post-operative data, performance reduced with Dice Coefficient of 0.54 and an IOU of 0. To improve the performance, the model's architecture is fine-tuned by introducing dilated convolutions and residual connections. This refinement yielded improvements in results, with a Dice Coefficient of 0.68 and an IOU of 0.62 in the post-operative context. This improvement underscores the need for further research to select and adapt efficient models, retrain specific layers with an extensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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AI-Enhanced Learning Assistant Platform 7

AI-Enhanced Learning Assistant Platform

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sumanth, N. Sai Priya, S. Vishnu Sankari, M. Kamatchi, K.S. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Sist Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. © 2024 IEEE.

关键词： Students

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ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared and Visible Images in Low Light

ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Duo Shi, Yiqi Zhang, Guoyin Li, Sizhao Zhang, Liguo School of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception. © 2025 IEEE.

关键词： Image Fusion Low Light Visually Enhanced Zero-Shot

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Feature-preserving color pencil drawings from photographs

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Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

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Retention Accelerated Testing for 3D QLC NAND Flash Memory: Characterization, Analysis, and Modeling

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第7期44卷 2779-2788页

作者： Yang, Shaoqi Zhang, Meng Zhan, Xuepeng Guo, Peng Zhao, Xiaohuan Yang, Guangkuo Guo, Xinyi Wu, Jixuan Wu, Fei Chen, Jiezhi Shandong University School of Information Science and Engineering Qingdao266237 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Shandong Sinochip Semiconductors Co. Ltd China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China

Three-dimensional (3D) NAND flash memory has become quite popular and is now widely used in data centers and mobile devices due to its outstanding storage density and cost-effectiveness. Larger storage capacity is made possible by 3D quad-level cell (QLC) NAND flash memory with the charge-trap (CT) structure, which stores four bits in each cell. However, data reliability is sacrificed in exchange for greater capacity. The lifespan of data retention is crucial for non-volatile storage. Thus, an important role is played by the Arrhenius model, which is widely used for lifespan prediction and high-temperature acceleration testing. Interestingly, we discover that the conventional Arrhenius model is inaccurate after analyzing the data retention properties of 3D QLC NAND flash memory. An empirical model is proposed for changing the apparent activation energy (Ea) based on the influence of different parameters, in order to accurately predict data lifespan and perform accelerated experiments. This developed model provides a temperature-and cycle-related parameter table for Ea, which is useful for high-temperature acceleration testing examinations. Simultaneously, we observe a linear connection between the 40°C data retention time mapping and the other temperatures. We evaluate the effects of the modified Ea model and the classic Arrhenius model with the epitaxial data and conclude that the former can reduce the error by approximately 70% to a maximum. © 1982-2012 IEEE.

关键词： Activation energy

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Extending Scratch Framework to Improve the Analytical Skills of Undergraduate Students 24

Extending Scratch Framework to Improve the Analytical Skills...

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10th International Conference on computer technology Applications, ICCTA 2024

作者： Akbar, Ubaid Ul Akbar, Saeed Ali, Mumtaz Ullah, Rahmat Khan, Rizwan Lopes, Ivandro Ortet Department of Computer Science City University of Science and Information Technology Peshawar Pakistan School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Computer Science and Electronic Engineering University of Essex United Kingdom School of Cyber Science & Engineering Huazhong University of Science & Technology Wuhan China

ISBN: (纸本)9798400716386

Scratch is an innovative and the most popular block-based Visual Programming Language designed for beginners to learn programming effectively. However, it lacks the capacity to allow users to learn and solve larger real-world problems such as the Travelling Salesman Problem (TSP). Hence, there is a need for an accessible and effective tool to assist beginners and novice programmers in learning and tackling the TSP. This paper introduces ScratchTSP, an extension of the Scratch programming Framework, specifically designed for the TSP. It offers an opportunity for beginners and novice programmers to grasp the TSP and the various algorithms used to solve it. For evaluation, we conduct a survey among undergraduate students to assess the usability and usefulness of the proposed ScratchTSP extension. Survey findings indicate that ScratchTSP offers a user-friendly and effective tool for beginners or novice programmers, aiding them in learning the TSP while developing their critical thinking and problem-solving skills. © 2024 ACM.

关键词： Students

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Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

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computer Modeling in engineering & sciences 2023年第11期137卷 2025-2047页

作者： Huabin Wang Ruichao Mo Yuping Chen Weiwei Lin Minxian Xu Bo Liu School of Computer Science and Engineering Huizhou UniversityHuizhou516007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science and Technology South China Normal UniversityGuangzhou510631China Department of New Network Technologies Peng Cheng LaboratoryShenzhen518066China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile ***,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy *** addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge ***,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model *** results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher ***,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

关键词： Pedestrian and vehicle detection YOLOv4 channel pruning cloud-edge collaboration

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Task Offloading and Scheduling under Hard Deadlines in Vehicular Edge Computing Systems

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IEEE Transactions on Vehicular technology 2025年第6期74卷 10029-10034页

作者： Gao, Kangyu Song, Jaeyoung Zhou, Changjun Zheng, Zhonglong Jeon, Sang-Woon Department of Electronics Engineering Hanyang University Ansan Korea Republic of Department of Electronics Engineering Pusan National University Busan Korea Republic of School of Computer Science and Technology Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China Department of Electrical and Electronic Engineering Hanyang University Ansan Korea Republic of

In this correspondence, we consider vehicular edge computing systems in which computing tasks arrive randomly to vehicles over time and are offloaded to one of the edge servers. Unlike previous work that assumes ideal and global server information, we study a practical online framework for acquiring edge server information. We propose an online offloading and scheduling algorithm that efficiently integrates the Moore–Hodgson algorithm into this practical framework to satisfy hard deadlines required by tasks. Numerical experiments demonstrate that the proposed algorithm outperforms conventional first-come-first-serve scheduling and D-Dedas scheduling. Furthermore, the results show that the performance of proactive broadcasting of server information closely approaches that of ideal server information as the number of vehicles increases. © 1967-2012 IEEE All rights reserved.

关键词： Scheduling algorithms

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Key Management and Authentication Scheme Forward Trusted Pervasive Container Computing 2

Key Management and Authentication Scheme Forward Trusted Per...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Hei, XinHong Xue, YuZhan Wang, YiChuan Liu, YiFan Peng, Chen Sun, Xiang School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies. © 2024 IEEE.

关键词： Cloud computing architecture

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Deep Fake Analyser: A Review Based on Detecting the Deepfakes 1

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Srivastava, Santosh Kumar Srivastava, Durgesh Kantha, Praveen Mahto, Manoj Kumar Kumar, Sheo Singh, Hare Ram Department of Computer Science and Engineering School of Computing Science and Engineering and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Pradesh Baddi India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering GNIOT Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It becomes very hard for a person to find out which of the media content is real and which is fake. With the help of deep learning and GANs (Generative Adversarial Networks), deep fakes can be produced. A Deep fake is a fake video or image where a person in an existing video or image is replaced by someone else by using an algorithm. If we look at the positives, we can see how deep fake technology can be used in advertisements, filmmaking, and video games. But on the contrary, it also results in the spread of fake news and the generation of pornographic content. This seriously impacts society and presents enormous threats. This research paper aims to present a paper based on a deep fake analyzer that will be able to identify and recognize fake media with the help of deep neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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