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Proceedings of the 23rd International Conference on software engineering

作者： John D. Tvedt Roseanne Tesoriero Kevin A. Gary The Catholic University of America (CUA) Department of EE & Computer Science 201 Pangborn Hall Washington DC CUA and Fraunhofer Center for Experimental Software Engineering College Park MD UNICON Inc. 3140 N. Arizona Ave. Suite 113 Chandler AZ

ISBN: (纸本)9780769510507

Industry often complains that current university curricula fail to address the practical issues of real software development. This paper outlines a proposal for an innovative core curriculum for a Bachelor of Science in computer Science. The proposed core curriculum contains elements of traditional computer science programs combined with software engineering via a team-oriented, hands-on approach to large-scale software development. In addition to traditional lecture/project/exam courses, students are required to take an eight-semester sequence of “software Factory” courses. software Factory courses put the students' newly acquired skills to work in a real software organization staffed and managed by all students in the program. Students from all courses in the software Factory sequence meet simultaneously to fulfill their roles in the software organization. We expect the students will be better-prepared software engineering practitioners after completing a curriculum that combines traditional courses with practical software Factory experience.

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software engineering and computer Systems 1

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丛书名： Communications in computer and Information Science

1000年

作者： Jasni Mohamad Zain Wan Maseri bt Wan Mohd Eyas El-Qawasmeh

ISBN: (数字)9783642221705

ISBN: (纸本)9783642221699

This Three-Volume-Set constitutes the refereed proceedings of the Second International Conference on software engineering and computer Systems, ICSECS 2011, held in Kuantan, Malaysia, in June 2011. The 190 revised full papers presented together with invited papers in the three volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on software engineering; network; bioinformatics and e-health; biometrics technologies; Web engineering; neural network; parallel and distributed; e-learning; ontology; image processing; information and data management; engineering; software security; graphics and multimedia; databases; algorithms; signal processing; software design/testing; e- technology; ad hoc networks; social networks; software process modeling; miscellaneous topics in software engineering and computer systems.

关键词： Information Systems Applications (incl. Internet) Algorithm Analysis and Problem Complexity software engineering computer Communication Networks Computation by Abstract Devices Pattern Recognition

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software engineering and computer Systems 1

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丛书名： Communications in computer and Information Science

1000年

作者： Jasni Mohamad Zain Wan Maseri bt Wan Mohd Eyas El-Qawasmeh

ISBN: (数字)9783642221910

ISBN: (纸本)9783642221903

This Three-Volume-Set constitutes the refereed proceedings of the Second International Conference on software engineering and computer Systems, ICSECS 2011, held in Kuantan, Malaysia, in June 2011. The 190 revised full papers presented together with invited papers in the three volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on software engineering; network; bioinformatics and e-health; biometrics technologies; Web engineering; neural network; parallel and distributed e-learning; ontology; image processing; information and data management; engineering; software security; graphics and multimedia; databases; algorithms; signal processing; software design/testing; e- technology; ad hoc networks; social networks; software process modeling; miscellaneous topics in software engineering and computer systems.

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Events and findings of the Purdue University Calumet computer Information Technology & Graphics (CIT & G) department teaching partnership with the College of software engineering of Southeast University in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

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10th ACM Special Interest Group for Information Technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast University of Nanjing China and Purdue University Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

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Attribute grouping-based naive Bayesian classifier

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Science China(Information Sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

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Multi-Scale Feature Optimization Point Cloud Completion Network Integrating SoftPool

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 233-243页

作者： Zhang, Wanpeng Zhou, Ziwei School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Point cloud completion is crucial in point cloud processing, as it can repair and refine incomplete 3D data, ensuring more accurate models. However, current point cloud completion methods commonly face a challenge: they fail to fully utilize multi-scale information from local features, leading to limitations in accuracy and detail preservation. To address this issue, this paper proposes a multi-scale feature optimization algorithm for point cloud completion that integrates SoftPool. Based on DGCNN, the method combines dilated convolution and bottleneck attention mechanisms to extract features at different scales, enhancing the ability to capture detailed information in point clouds. The bottleneck attention mechanism is used to optimize important detail features. The extracted local features are concatenated with their corresponding positional information to form point proxies, enhancing the effective extraction of local geometric features, resulting in more refined completed point cloud shapes. A Transformer architecture is employed to model these features. Finally, SoftPool is introduced for fine-grained feature downsampling, improving the network's ability to recover point cloud details. FoldingNet is used to reconstruct missing structures and output the completed point cloud. To validate the model's completion performance, training and testing are conducted on the PCN and ShapeNet55 datasets. Experimental results demonstrate that the model has better feature detail retention and more accurate completion results. On the PCN dataset, the average CD value is reduced by 6.5% compared to the best-performing model among the comparison methods. On the ShapeNet55 dataset, the average CD value across three difficulty levels is reduced by 6.9% compared to the best-performing model among the comparison methods. Additionally, the model also achieved a 2.1% improvement in F-score. © (2025), (International Association of Engineers). All rights reserved.

关键词： Ability testing

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Improving UAV Image Target Detection: A Novel Approach Using OptiDETR with Swin Transformer

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 771-780页

作者： Ma, Wenlong Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, and varying lighting conditions, all of which exacerbate the difficulty of recognition. In recent years, the DETR model based on the Transformer architecture has eliminated traditional post-processing steps such as NMS(Non-Maximum Suppression), thereby simplifying the object detection process and improving detection accuracy, which has garnered widespread attention in the academic community. However, DETR has limitations such as slow training convergence, difficulty in query optimization, and high computational costs, which hinder its application in practical fields. To address these issues, this paper proposes a new object detection model called OptiDETR. This model first employs a more efficient hybrid encoder to replace the traditional Transformer encoder. The new encoder significantly enhances feature processing capabilities through internal and cross-scale feature interaction and fusion logic. Secondly, an IoU (Intersection over Union) aware query selection mechanism is introduced. This mechanism adds IoU constraints during the training phase to provide higher-quality initial object queries for the decoder, significantly improving the decoding performance. Additionally, the OptiDETR model integrates SW-Block into the DETR decoder, leveraging the advantages of Swin Transformer in global context modeling and feature representation to further enhance the performance and efficiency of object detection. To tackle the problem of small object detection, this study innovatively employs the SAHI algorithm for data augmentation. Through a series of experiments, It achieved a significant performance improvement of more than two percentage points in the mAP (mean Average Precision) metric compared to current mainstream object detection models. Furthermore, ther

关键词： Decoding

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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VAT: Visibility Aware Transformer for Fine-Grained Clothed Human Reconstruction

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2277-2287页

作者： Zhang, Xiaoyan Zhu, Zibin Xie, Hong Ren, Sisi Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China

In order to reconstruct 3D clothed human with accurate fine-grained details from sparse views, we propose a deep cooperating two-level global to fine-grained reconstruction framework that constructs robust global geometry to guide fine-grained geometry learning. The core of the framework is a novel visibility aware Transformer VAT, which bridges the two-level reconstruction architecture by connecting its global encoder and fine-grained decoder with two pixel-aligned implicit functions, respectively. The global encoder fuses semantic features of multiple views to integrate global geometric features. In the fine-grained decoder, visibility aware attention mechanism is designed to efficiently fuse multi-view and multi-scale features for mining fine-grained geometric features. The global encoder and fine-grained decoder are connected by a global embeding module to form a deep cooperation in the two-level framework, which provides global geometric embedding as a query guidance for calculating visibility aware attention in the fine-grained decoder. In addition, to extract highly aligned multi-scale features for the two-level reconstruction architecture, we design an image feature extractor MSUNet, which establishes strong semantic connections between different scales at minimal cost. Our proposed framework is end-to-end trainable, with all modules jointly optimized. We validate the effectiveness of our framework on public benchmarks, and experimental results demonstrate that our method has significant advantages over state-of-the-art methods in terms of both fine-grained performance and generalization. © 2025 IEEE.

关键词： Decoding

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