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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Bonita Sharif Kang-il Park Michael P. DeJournett Isaac Baysinger Mohammed Aly Jonathan I. Maletic School of Computing University of Nebraska - Lincoln Lincoln Nebraska USA Software Engineering Department Assiut University Assiut Egypt Department of Computer Science Kent State University Kent Ohio USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

A controlled experiment investigating the effect layout has on how students find defects in UML class diagrams with respect to requirements is presented. Two layout schemes from prior literature namely, multi-cluster and orthogonal layouts, are compared with respect to two open source systems, Doxygen and Qt. The experiment is conducted with 89 students from two universities in a classroom lab setting. Each participant is placed in one of two groups where each group are given 2 defect detection tasks (with five sub-parts) with each task using one of the two layouts in each subject system. The only difference between groups is that the layouts were flipped between the two tasks. Feedback is collected after each task. A mental rotation and object memory task is conducted at the end of the two tasks to correlate their spatial and working memory skills to the task performance. Results indicate that the multi-cluster layout performed better in terms of accuracy of finding defects, but not significantly. There is also not much difference in time to find them. Furthermore, it is found that the object memory skills are sometimes correlated with the performance of the defect detection tasks. These results can be used to help improve the teaching of UML class diagram defect detection skills by incorporating clustered layouts and object memory tasks. In addition, they can help identify people who are best suited for finding critical defects in design.

关键词： Visualization Accuracy Layout Unified modeling language Education software Defect detection

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis

Empowering Precision Machine: Deep Learning Technique for Lu...

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computing and Data Analytics (ICCDA), International Conference on

作者： Danish Jamil Syed Mehr Ali Shah Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed AlJarwani Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Sultanat of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics.

关键词： Deep learning Technological innovation Computed tomography Computational modeling Precision medicine Lungs Lung cancer Medical services Data models Medical diagnostic imaging

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Predicting Lung Cancer Risk Using Artificial Intelligence

Predicting Lung Cancer Risk Using Artificial Intelligence

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computing and Data Analytics (ICCDA), International Conference on

作者： Syed Mehr Ali Shah Danish Jamil Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed Al-Jarwani College of Computing & Information Sciences University of Technology and Applied Sciences shinas Sultanat of Oman Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detection

关键词： Surveys Analytical models Ethics Accuracy Linear regression Lung cancer Convolutional neural networks Medical diagnosis Artificial intelligence X-ray imaging

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DYNAMIC DATA SAMPLER FOR CROSS-LANGUAGE TRANSFER LEARNING IN LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Li, Yudong Feng, Yuhao Zhou, Wen Zhao, Zhe Shen, Linlin Hou, Cheng Hou, Xianxu School of Computer Science and Software Engineering Shenzhen University China Tencent AI Lab China LIESMARS Wuhan University China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool China

Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the difficulty in acquiring large-scale corpus and the requisite computing resources. In this paper, we propose ChatFlow, a cross-language transfer-based LLM, to address these challenges and train large Chinese language models in a cost-effective manner. We employ a mix of Chinese, English, and parallel corpus to continuously train the LLaMA2 model, aiming to align cross-language representations and facilitate the knowledge transfer specifically to the Chinese language model. In addition, we use a dynamic data sampler to progressively transition the model from unsupervised pre-training to supervised fine-tuning. Experimental results demonstrate that our approach accelerates model convergence and achieves superior performance. We evaluate ChatFlow on popular Chinese and English benchmarks, the results indicate that it outperforms other Chinese models post-trained on LLaMA-2-7B. © 2024, CC BY.

关键词： Cost effectiveness

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MFN: Explainable DNA triple helixes Stabilized Design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoru Wen Lijun Sun Lei Xie Yanfen Zheng Ben Cao Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy.

关键词： Scalability Design methodology DNA Crosstalk Genomics Games Reliability engineering Regulation Gene therapy Bioinformatics

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Personalized Federated Learning with Multiple Classifier Aggregation

SSRN

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SSRN 2024年

作者： Zheng, Shaifeng Zhu, Qingling Lin, Qiuzhen Liu, Songbai Wong, Ka-Chun Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science City University of Hong Kong Hong Kong

Personalized federated learning (PFL) has garnered attention due to its capability to address statistical heterogeneity among clients. Typically, prevailing PFL methods aggregate a single global model for personalization, which may be inadequate for clients with diverse data distributions. Furthermore, in the local update, each private dataset is used to optimize the model independently, which increases the risk of overfitting the current data distribution and losing previously acquired knowledge, resulting in knowledge forgetting. In this study, personalized federated learning with multiple classifier aggregation is proposed. The method involves splitting the client model into its head and base, then optimizing them respectively using an alternating optimization strategy that first optimizes the head and then the base. Initially, to address the suboptimal model problem, the proposed approach aggregates multiple classifiers using data distribution and employs knowledge distillation to impart positive and negative classifier knowledge for learning the most suitable personalized model head. Additionally, to mitigate knowledge forgetting, a learnable personalization layer is introduced, and hidden loss is utilized to learn the knowledge of the global base and prevent overfitting of the model base. Empirical results demonstrate that the proposed method achieves competitive performance across various benchmarks, outperforming the majority of state-of-the-art PFL algorithms. The source code is publicly available at \href{https://***/xiaye-maker/FedMCA}{https://***/xiaye-maker/FedMCA} © 2024, The Authors. All rights reserved.

关键词： Aggregates

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The Applications of Personalized Micro-videos in Computer Network Course Teaching 11

The Applications of Personalized Micro-videos in Computer Ne...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Fu, Wenxiang Yu, Xiaomei Gong, Zhaokun Che, Xueyu Mao, Qian Li, Yan Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory of Distributed Computing Software Jinan China

ISBN: (纸本)9781665406796

Nowadays, the computer network course becomes one of the core compulsory courses for computer related majors. This paper analyzes the problems existing in computer network course teaching. And then, aiming at the problems of low utilization of curriculum resources and poor classroom learning effect, this paper presents the method of improving teaching mode based on personalized micro-videos from the perspective of educational equity. Specially, a personalized micro-videos teaching model based on autonomous learning and collaborative learning is constructed. Taking 'home network construction' in the course of computer network in vocational education as an example, the course is designed and the teaching practice is carried out. The results of teaching practice have proved that the novel teaching model improves the teaching effect and raises the learning enthusiasm of students greatly. © 2021 IEEE.

关键词： Teaching

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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HFGS: 4D Gaussian Splatting with Emphasis on Spatial and Temporal High-Frequency Components for Endoscopic Scene Reconstruction

arXiv

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arXiv 2024年

作者： Zhao, Haoyu Zhao, Xingyue Zhu, Lingting Zheng, Weixi Xu, Yongchao School of Computer Science Wuhan University Hubei China School of Software Engineering Xi'an Jiaotong University Shaanxi China School of Computing and Data Science The University of Hong Kong Hong Kong

Robot-assisted minimally invasive surgery benefits from enhancing dynamic scene reconstruction, as it improves surgical outcomes. While Neural Radiance Fields (NeRF) have been effective in scene reconstruction, their slow inference speeds and lengthy training durations limit their applicability. To overcome these limitations, 3D Gaussian Splatting (3D-GS) based methods have emerged as a recent trend, offering rapid inference capabilities and superior 3D quality. However, these methods still struggle with under-reconstruction in both static and dynamic scenes. In this paper, we propose HFGS, a novel approach for deformable endoscopic reconstruction that addresses these challenges from spatial and temporal frequency perspectives. Our approach incorporates deformation fields to better handle dynamic scenes and introduces Spatial High-Frequency Emphasis Reconstruction (SHF) to minimize discrepancies in spatial frequency spectra between the rendered image and its ground truth. Additionally, we introduce Temporal High-Frequency Emphasis Reconstruction (THF) to enhance dynamic awareness in neural rendering by leveraging flow priors, focusing optimization on motion-intensive parts. Extensive experiments on two widely used benchmarks demonstrate that HFGS achieves superior rendering quality. Source code is available at https://***/zhaohaoyu376/HFGS. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge computing Networks

Deep Reinforcement Learning for Joint Service Placement and ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

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