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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

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.

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Comparative analysis of various models for image classification on Cifar-100 dataset

Comparative analysis of various models for image classificat...

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2023 International Conference on Machine Learning and Automation, CONF-MLA 2023

作者： Zheng, YuYu Huang, HaoXuan Chen, JunMing School of Optoelectronic Information and Computer Engineering University of Shanghai for Science and Technology ShangHai200093 China Department of Computing and Software McMaster University HamiltonL8S4L8 Canada Department of Mathematics University of California Los Angeles Los Angeles90095 United States

Nowadays, people developed various convolutional neural network (CNN) based models for computer vision. Some famous models, such as GoogLeNet, Residual Network (ResNet), Visual Geometry Group (VGG), and You Only Look Once (YOLO), have different architecture and performances. Determining which model to use may be a troublesome problem for those just starting to study image classification. To solve this problem, we introduce the GoogLeNet, ResNet-18, and VGG-16 models, comparing their architecture, features, and performance. Then we give our suggestions based on the test results to help beginners choose a suitable model. We conducted experiments to train and test GoogLeNet, ResNet-18, and VGG-16 on the Cifar-100 datasets with the same hyperparameters. Based on the test results (test accuracy, average test loss, training loss), we analyze the figures for trends, key points, increase rate, and other features. Then we combine the architecture of each model to make our conclusions. The experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time complexity. ResNet-18 also has the fastest convergence speed. GoogLeNet would be the second choice because it functions similarly to ResNet-18 and is even better. However, training GoogLeNet is a time-consuming task. VGG is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Image classification

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MetBench: A Numerical Expression Metamorphic Relations Benchmark Data Set

MetBench: A Numerical Expression Metamorphic Relations Bench...

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Yin Zhao Meng Li Yongcheng Su Kailin Zi Cong Zeng School of Computing University of South China Hengyang China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

Metamorphic testing is one of the most effective methods to alleviate the oracle problem. However, the lack of benchmark data sets has hindered the development of metamorphic testing. This study identifies and extracts key characteristics from three entities: the business domain, applications, and metamorphic relations. It then establishes a model for numerical expression metamorphic relations. Data were gathered from open source programs, academic papers, and industrial software, which resulted in the construction of the MetBench benchmark data set encompassing 139 codes and 924 metamorphic relations. In addition, we developed a general purpose tool for automating the execution of metamorphic testing, which separates code, metamorphic relations, and testing processes. This tool supports the management of metamorphic relations, the automated execution of metamorphic testing, and the visualization of the results. Specifically, metamorphic relations are encoded in the literal format of LaTeX and the computational format of SymPy and are stored using the document-oriented database LiteDB. The system utilizes a combination of C# and Python programming languages. The former is responsible for the user interface, while the latter handles the automated execution. Visualization is achieved through Matplotlib. The establishment of the MetBench benchmark data set facilitates the study of metamorphic relation identification techniques, the evaluation of the effectiveness of relations, and their nature characteristics such as complexity, similarity, and false satisfaction rate.

关键词： Codes Databases Data visualization Prototypes Benchmark testing software C# languages Numerical models Optimization Python

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Automatic Deployment to Kubernetes Cluster by Applying a New Learning Tool and Learning Processes 10

Automatic Deployment to Kubernetes Cluster by Applying a New...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Golis, Tomáš Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia

The rapid growth and widespread adoption of containerization technologies, such as Docker, and the increasing popularity of Kubernetes as a container orchestration platform have significantly shaped the landscape of modern software development and deployment. Containers have created a paradigm shift in application packaging, offering a consistent and portable environment across diverse infrastructure setups. Meanwhile, Kubernetes has emerged as the dominant choice for managing containerized applications at scale, providing features such as automated scaling, load balancing, and fault tolerance. However, despite the numerous advantages containers and Kubernetes offer, deploying applications to a Kubernetes cluster often poses challenges due to the steep learning curve and complex configuration requirements. To address these challenges, this paper aims to introduce a comprehensive learning tool that automates and simplifies the deployment process on Kubernetes clusters. © 2023 Copyright for this paper by its authors.

关键词： CI/CD Container orchestration Docker image Kubernetes

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Financial Sustainability of Automotive software Compliance and Industry Quality Standards 8th

Financial Sustainability of Automotive Software Compliance ...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Dakić, Pavle Todorović, Vladimir Vranić, Valentino Faculty of Informatics and Computing Singidunum University Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Business Studies and Law MB University Belgrade Serbia

ISBN: (纸本)9789819930906

Achieving a high level of quality in the AV industry involves understanding the complexity of the production cycle with the constant challenges of applying appropriate standards. The need for compliance based on relevant certificates implies the creation of an environment of trust between manufacturers, component suppliers, and end customers. Research should sublimate the most current standards from various fields related to data protection, security, system sustainability, environmental management, financial sustainability, and business transparency. The success of a complex parts supply system is impossible without a stable approach to IT security. The control of production processes sometimes involves using non-standardized techniques, and one of the applicable ones is work-in-progress (WIP), which describes the use of partially finished raw materials in the stages of obtaining final products. Key points and the contribution of the current study are in getting a better overview of the situation within this area related to standards and the financial problems that can arise due to a lack of understanding of standards compliance. The main limitations within the study itself refer to the consideration of the theoretical foundations that should be used during future research related to the practical part during implementation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

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AI-Powered IT Project Management: Analyzing the Effectiveness of Advanced Project Management Tools to Ensure Project Efficiency

AI-Powered IT Project Management: Analyzing the Effectivenes...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Das, Jiban Krisna Elegbe, Ifeoluwa Coffie, Lord Khadka, Rojeena Chen, Lei Ji, Yiming Georgia Southern University Dept. of Information Technology StatesboroGA United States College of Computing and Software Engineering Kennesaw State University United States

ISBN: (纸本)9798331504847

The integration of Artificial Intelligence (AI) into project management has significantly transformed traditional methodologies, particularly within the Information Technology (IT) sector. This study investigates the effectiveness of AI-powered tools, specifically Asana, Jira, and *** to improve project management efficiency through automation, resource optimization, and decision-making processes. Using a systematic literature review and quantitative analysis, the research highlights the capabilities and limitations of these tools in the management of complex IT projects. The study findings reveal that AI-powered platforms facilitate real-time tracking, predictive analytics and dynamic resource allocation, which collectively contribute to reduced project timelines and improved outcomes. However, challenges such as data quality, integration complexities, and the need for skilled personnel remain prevalent. The study was summarized with strategic recommendations for organizations to incorporate automation, leverage predictive analytics, and improve data integrity, thus maximizing the benefits of AI in project management. This research underscores the critical role of AI in modernizing project management practices, which ultimately leads to increased productivity and project success rates in the IT domain. © 2025 IEEE.

关键词： Project management

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SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Nagib, Ahmad M. Abou-Zeid, Hatem Hassanein, Hossam S. School of Computing Queen’s University Canada Department of Electrical and Software Engineering University of Calgary Canada Faculty of Computers and Artificial Intelligence Cairo University Egypt

Deep reinforcement learning (DRL)-based slicing policies have shown significant success in simulated environments but face challenges in physical systems such as open radio access networks (O-RANs) due to simulation-to-reality gaps. These policies often lack safety guarantees to ensure compliance with service level agreements (SLAs), such as the strict latency requirements of immersive applications. As a result, a deployed DRL slicing agent may make resource allocation (RA) decisions that degrade system performance, particularly in previously unseen scenarios. Real-world immersive applications require maintaining SLA constraints throughout deployment to prevent risky DRL exploration. In this paper, we propose SafeSlice to address both the cumulative (trajectory-wise) and instantaneous (state-wise) latency constraints of O-RAN slices. We incorporate the cumulative constraints by designing a sigmoid-based risk-sensitive reward function that reflects the slices’ latency requirements. Moreover, we build a supervised learning cost model as part of a safety layer that projects the slicing agent’s RA actions to the nearest safe actions, fulfilling instantaneous constraints. We conduct an exhaustive experiment that supports multiple services, including real virtual reality (VR) gaming traffic, to investigate the performance of SafeSlice under extreme and changing deployment conditions. SafeSlice achieves reductions of up to 83.23% in average cumulative latency, 93.24% in instantaneous latency violations, and 22.13% in resource consumption compared to the baselines. The results also indicate SafeSlice’s robustness to changing the threshold configurations of latency constraints, a vital deployment scenario that will be realized by the O-RAN paradigm to empower mobile network operators (MNOs). Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model

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IEEE Transactions on Services computing 2025年第3期18卷 1445-1458页

作者： Yinchen Pan Jun Zeng Ziwei Wang Haoran Tang Junhao Wen Min Gao School of Big Data and software engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong

In recent years, next Point-of-Interest (POI) recommendation is essential for many location-based services, aiming to predict the most likely POI a user will visit next. Current research employs graph-based and sequential methods, which have significantly improved performance. However, there are still limitations: numerous methods overlook the fact that user intent is constantly changing and complex. Furthermore, prior studies have seldom addressed spatiotemporal correlations while considering differences in user behavior patterns. Additionally, implicit feedback contains noise. To address these issues, we propose a recommender model named HGDRec for the next POI recommendation. Specifically, we introduce an approach for extracting trajectory intent by integrating multi-dimensional trajectory representations to achieve a multi-level understanding of user trajectories. Then, by analyzing users’ long trajectories, we construct global hypergraph structures across spatiotemporal regions to comprehensively capture user behavior patterns. Additionally, to further optimize trajectory intent representation, we employ a feature optimization method based on the improved diffusion model. Extensive experiments on three real-world datasets validate the superiority of HGDRec over the state-of-the-art methods.

关键词： Trajectory Diffusion models Spatiotemporal phenomena Feature extraction Optimization Transformers Data mining Noise measurement Data models Correlation

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Exploring Aspects of Agile software Development Risk – Results from a MLR 1

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29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Nolan, Aaron Strickland, Ben Quinn, Adam Gallagher, Kyle Yilmaz, Murat Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey Lero the Science Foundation Ireland Research Center for Software Lero Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Agile software development methods are widely used by software organisations, focusing on short developmental life cycles and customer satisfaction through the iterative and incremental development of software products. Despite their popularity, these methods present risks that may be underappreciated. This paper examines certain risks attributed to agile software development, with a focus on the lack of documentation, scope creep, technical debt and job satisfaction. Through the application of a multivocal literature review, we find that agile software development can greatly benefit projects. However, when agile methods are implemented inappropriately or sub-optimally, projects risk over-spending, delayed or defective software, employee turnover, and overall decreased productivity. Understanding the risks associated with agile software development can help practitioners to achieve higher efficiency and success in their software development projects. © 2022, Springer Nature Switzerland AG.

关键词： Job satisfaction

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