Proceedings of the 5th International Conference on Computer Science and Software Engineering最新文献

Protein Folding Structure Prediction using Reinforcement Learning with Application to Both 2D and 3D Environments 基于强化学习的蛋白质折叠结构预测及其在二维和三维环境中的应用

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570102

Jason Lu

{"title":"Protein Folding Structure Prediction using Reinforcement Learning with Application to Both 2D and 3D Environments","authors":"Jason Lu","doi":"10.1145/3569966.3570102","DOIUrl":"https://doi.org/10.1145/3569966.3570102","url":null,"abstract":"Proteins are critical for lives. They not only build 10%-35% of our body tissues, but also can be used to understand the structures of different viruses, and then help us to explore effective vaccines. Hence, predicting new protein structures is very important for human health. However, the structure of protein is complicated. Exploration using human experiments is cost-consuming. Recently, artificial intelligence (AI) technology, such as imitation learning and reinforcement learning (RL), has been rapidly developed and significantly improved the efficiency in many different domains. In this project, we will try to use RL to solve the protein folding structure prediction problem. First, we adopted the PH structure as a relatively simple representation of the protein structure, where different peptides can be categorized into two types: P(hydrophilic) and H(hydrophobic). The goal of the protein folding is to try to make more H pairs during the folding process. We then formulated the protein folding problem as a reinforcement learning process. If a new H pair is generated during folding, we collect -1 reward. Such RL reward is designed based on the protein dataset (Protein Data Bank). Finally, we implemented three RL algorithms: 1) Q-learning, 2) Deep Q-learning, and 3) Double Deep Q-learning (DDQN). We implemented and compared the three algorithms in terms of their accuracy and efficiency. We found that all three algorithms can accurately predict the structures of simple proteins. As protein structures become more complicated, the DDQN is performing better.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127179506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software quality evaluation based on improved RAD model and AHP 基于改进的RAD模型和AHP的软件质量评价

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570036

Zuchuang Zheng, Shanliang Xue, Meijiao Xu, Mao-sheng Li, Ruxue Ma

引用次数: 0

Research on Image Information Restoration Algorithm of Printing Micro Dots Based on GAN 基于GAN的印刷微点图像信息恢复算法研究

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3571169

Bo Yuan, Peng Cao

{"title":"Research on Image Information Restoration Algorithm of Printing Micro Dots Based on GAN","authors":"Bo Yuan, Peng Cao","doi":"10.1145/3569966.3571169","DOIUrl":"https://doi.org/10.1145/3569966.3571169","url":null,"abstract":"During printing and shooting, the degradation of printing micro dots significantly affects the decoding and reading of hidden anti-counterfeiting information. However, existing image restoration methods cannot effectively restore image information. Moreover, there are relatively few datasets related to halftone dot images, and most datasets differ from the real data. Therefore, we propose an end-to-end restoration model based on the single-image super-resolution information. Specifically, we constructed a PMD dataset for real printing of anti-counterfeiting scenes. Based on this dataset, we used the high-resolution image information as the target. The positional inclination of the degraded images is corrected using the blank and interline characteristics of the printing micro dots images. The restoration is completed with the help of feature extraction and upsample of ESRGAN. In addition, we propose evaluation measures suitable for error detection, correction, and decoding requirements for microscopic image information. The experimental results show that, within the noise tolerance range, the image information restored by our method has a maximum average bit error rate is 0.97% and a Euclidean distance is 0.00804 pixels, whereas traditional filtering measures cannot effectively restore image information. The experimental results verified the effectiveness and robustness of the proposed method.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114948762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective software test case selection based on density analysis 基于密度分析的多目标软件测试用例选择

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570010

Huihui Jia, Cheng Zhang, Sijie Wu

引用次数: 0

A Failure Prediction Approach Supporting Multi Granularity Data Fusion for Large-scale Cloud Storage Systems 支持大规模云存储系统多粒度数据融合的故障预测方法

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570119

Yongyang Cheng, T. Zhang, Jing Luo

{"title":"A Failure Prediction Approach Supporting Multi Granularity Data Fusion for Large-scale Cloud Storage Systems","authors":"Yongyang Cheng, T. Zhang, Jing Luo","doi":"10.1145/3569966.3570119","DOIUrl":"https://doi.org/10.1145/3569966.3570119","url":null,"abstract":"With the development of cloud computing and cloud storage technology, the data scale has grown rapidly. In order to store and process large-scale data, there are thousands of nodes and devices in the cloud storage center, resulting in a surge in the frequency of failures. In various types of failure events, storage device failure is the most important one. However, most cloud storage systems lack disk failure prediction mechanisms and could only replace disks after disk failures. It is particularly important to predict the potential risks in the system operation environment. In this paper, we propose a disk failure prediction approach that supports multi granularity data fusion, which solves problems of unbalanced samples, single data source, cross scenario model migration and insufficient generalization ability of prediction models in disk failure prediction. Through our proposed approach, the cloud storage system could accurately predict disk failures and actively push prediction results to users, so as to improve the pertinence and planning of the operation and maintenance work. The approach presented in this paper has been validated to be valid through a series of qualitative and quantitative experiments.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129641877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Fault Diagnosis Method for Reactor Primary Circuit System Based on multi-source information fusion 基于多源信息融合的电抗器一次回路系统故障诊断方法研究

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570079

Jie Ma, Zhuang Han, Qiao Peng

引用次数: 0

MetaCNN: A New Hybrid Deep Learning Image-based Approach for Vehicle Classification Using Transformer-like Framework MetaCNN:一种基于混合深度学习图像的基于变压器框架的车辆分类方法

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570099

Juntian Chen, Ruikang Luo

{"title":"MetaCNN: A New Hybrid Deep Learning Image-based Approach for Vehicle Classification Using Transformer-like Framework","authors":"Juntian Chen, Ruikang Luo","doi":"10.1145/3569966.3570099","DOIUrl":"https://doi.org/10.1145/3569966.3570099","url":null,"abstract":"Abstract—With the development of vehicles and traffic system in the early 21st century, the need for a monitored traffic system and vehicle classification is enlarging. Together with the development of deep learning, computer vision realm has emerged versatile models that is able to fulfill the need of classification. Those popular models include CNN, Vision Trans- former, Metaformer and so on. However, these models handle the problem based on different data processing techniques, they either lacks efficiency or effectiveness. In particular, CNN is shortcoming in global data while ViT is lack of extraction of local information. Therefore, based on this research gap, we proposed a model called MetaCNN, which combines CNN and Poolformer – a specific metaformer structure, which takes the strength of the two models and compensate for both models’ deficiencies. Finally, in order to verify the feasibility of our model, we tested our model on a real-world remote sensing datasets of vehicle images in six different regions with different weather conditions. Our model MetaCNN has demonstrated better recognition performance compared to other baseline models. The results further prove that our model MetaCNN is adept at vehicle classification of remote sensing images though under complex scenarios","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115931653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAKT: A Knowledge Tracing Model Based on Meta Path and Attention Mechanism 基于元路径和注意机制的知识跟踪模型

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3569987

Shaopeng Yang, Tiancheng Zhang, Siyuan Mao, Gensitskiy Yu., Yiming Sun

{"title":"MAKT: A Knowledge Tracing Model Based on Meta Path and Attention Mechanism","authors":"Shaopeng Yang, Tiancheng Zhang, Siyuan Mao, Gensitskiy Yu., Yiming Sun","doi":"10.1145/3569966.3569987","DOIUrl":"https://doi.org/10.1145/3569966.3569987","url":null,"abstract":"With the deep integration of artificial intelligence technology and education, the traditional educational pattern has changed hugely. And the adaptive learning based on automatically tracing the knowledge status of students at various stages has attracted much attention. As a key technology, knowledge tracing has become an important research. Although deep learning has been used in knowledge tracing and promoted certain performance improvement, it still has drawbacks. First, current researches consider less the explicit representation of meta path between users, exercise items and knowledge points, ignoring some of the higher-order information. Secondly, the effect of higher-order information of knowledge points on prediction is ignored. Therefore, we proposes a meta-path based four-way co-attention mechanism model MAKT to inversely infer the unobservable knowledge cognitive proficiency of learners. Based on meta path, the MAKT model integrates instance information and higher-order information between nodes to effectively enhance the representation of user, exercise item and knowledge points. The effectiveness of the model was demonstrated in tests on a real data set.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of cervical vertebrae from infrared thermal imaging based on improved Yolo v3 基于改进Yolo v3的颈椎红外热成像检测

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570059

Yaqun Wang, Di Sun, Lei Liu, Luan Ye, Kaidi Fu, Xinyu Jin

{"title":"Detection of cervical vertebrae from infrared thermal imaging based on improved Yolo v3","authors":"Yaqun Wang, Di Sun, Lei Liu, Luan Ye, Kaidi Fu, Xinyu Jin","doi":"10.1145/3569966.3570059","DOIUrl":"https://doi.org/10.1145/3569966.3570059","url":null,"abstract":"Yolo has achieved great success in the field of image segmentation, and has been applied to infrared thermal imaging detection. However, in the feature pyramid for feature fusion, high-level spatial feature information is lost, and both high-level and low-level features have poor semantics. This paper proposes an infrared thermal imaging cervical spine part extraction method based on improved Yolo v3. In order to make up for the channel information lost in feature fusion, this paper convolves the high-level features, and then enhances the residual features to reduce the semantic loss caused by the number of channels by compensating for the spatial context information. To reduce the semantic gap of additive fusion, this paper applies an attention mechanism on low-level features. The improved Yolo v3 algorithm was used to extract the cervical vertebrae in infrared thermal images, and comparative experiments were completed. Experiments on the dataset collected in the cooperative hospital demonstrate that our proposed improved Yolo v3 achieves better performance.","PeriodicalId":145580,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125960194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Image Enhancement Filtering Algorithm for Speckle Patterns 一种斑点图像增强滤波算法

Proceedings of the 5th International Conference on Computer Science and Software Engineering Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570043

Boyuan Yao, Ying Wu

引用次数: 0