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35th IEEE International Symposium on software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Yichi Feng, Yang Ju, Jianxun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Peking University Beijing China *** Group Ltd Shanghai China

ISBN: (纸本)9798350367041

With advantages such as flexibility and adaptability, agile has become the mainstream methodology in modern software development. This paper investigates the challenges of implementing Agile software development in large software companies. To this end, we distributed questionnaires to 31 respondents and analyzed the responses. The collected feedback allowed us to identify eight key findings that elucidate the critical obstacles and areas requiring attention in large-scale Agile practices. Additionally, we formulated actionable suggestions to enhance the effectiveness of Agile methodologies within large corporate environments. Our study offers valuable insights and practical recommendations for practitioners and organizations aiming to optimize their Agile adoption and implementation strategies. The findings contribute to a deeper understanding of the unique challenges faced by large software companies and provide a foundation for future research in this area. © 2024 IEEE.

关键词： agile challenge investigation empirical study

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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A missing physical fitness test data classification method based on MLP 10

A missing physical fitness test data classification method b...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Che, Peng Peng, Zhenlian Cao, Buqing Liu, Jianxun Chen, Tieping Fan, Runqing Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %. © 2023 IEEE.

关键词： Classification (of information)

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INSERTNERF: INSTILLING GENERALIZABILITY INTO NERF WITH HYPERNET MODULES 12

INSERTNERF: INSTILLING GENERALIZABILITY INTO NERF WITH HYPER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Bao, Yanqi Ding, Tianyu Huo, Jing Li, Wenbin Li, Yuxin Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Applied Sciences Group Microsoft Corporation Redmond United States

Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. Code will be available at: https://***/bbbbby-99/InsertNeRF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation 49th

ShadowAQP: Efficient Approximate Group-by and Join Query via...

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49th International Conference on Very Large Data Bases, VLDB 2023

作者： Gu Gurong@***, Rong Lihan.li@***, Han Dai Haipengdai@***, Haipeng Huang, Wenjie Xue, Jie Li, Meng Zheng, Jiaqi Cai, Haoran Huang, Yihua Chen, Guihai State Key Laboratory for Novel Software Technology Nanjing University China New York University Shanghai China

Approximate query processing (AQP) is one of the key techniques to cope with big data querying problem on account that it obtains approximate answers efficiently. To address non-trivial sample selection and heavy sampling cost issues in AQP, we propose ShadowAQP, an efficient and accurate approach based on attribute-oriented sample size allocation and data generation. We select samples according to group-by and join attributes, and determine the sample size for each group of unique value combinations to improve query accuracy. We design a conditional variational autoencoder model with automatic table data encoding and model update strategies. To further improve accuracy and efficiency, we propose a set of extensions, including parallel multi-round sampling aggregation, data outlier-aware sampling, and dimension reduction optimization. Evaluation results on diversified datasets show that, compared with SOTA approaches, ShadowAQP achieves 5.8× query speed performance improvement on average (up to 12.8×), while reducing query error by 74% on average (up to 95%) at the same time. © 2023, VLDB Endowment. All rights reserved.

关键词： Sampling

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HYBRID SHARING FOR MULTI-LABEL IMAGE CLASSIFICATION 12

HYBRID SHARING FOR MULTI-LABEL IMAGE CLASSIFICATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Yin, Zihao Gan, Chen He, Kelei Gao, Yang Zhang, Junfeng Medical School of Nanjing University China State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China

Existing multi-label classification methods have long suffered from label heterogeneity, where learning a label obscures another. By modeling multi-label classification as a multi-task problem, this issue can be regarded as a negative transfer, which indicates challenges to achieve simultaneously satisfied performance across multiple tasks. In this work, we propose the Hybrid Sharing Query (HSQ), a transformer-based model that introduces the mixture-of-experts architecture to image multi-label classification. HSQ is designed to leverage label correlations while mitigating heterogeneity effectively. To this end, HSQ is incorporated with a fusion expert framework that enables it to optimally combine the strengths of task-specialized experts with shared experts, ultimately enhancing multi-label classification performance across most labels. Extensive experiments are conducted on two benchmark datasets, with the results demonstrating that the proposed method achieves state-of-the-art performance and yields simultaneous improvements across most labels. The code is available at this URL. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image classification

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A Combinatorial Interaction Testing Method for Multi-Label Image Classifier

A Combinatorial Interaction Testing Method for Multi-Label I...

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International Symposium on software Reliability Engineering (ISSRE)

作者： Peng Wang Shengyou Hu Huayao Wu Xintao Niu Changhai Nie Lin Chen State Key Laboratory for Novel Software Technology and School of Computer Science Nanjing University China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Multi-label image classification is a critical task in computer vision, in which the correlations between labels are typically exploited by modern classifiers for an effective classification. In this study, we propose LV-CIT, a black-box testing method that applies Combinatorial Interaction Testing (CIT) to systematically test the ability of classifiers to handle such correlations. Specifically, LV-CIT views each label of the label space as an input-parameter taking binary values (indicating whether an object appears in an image), and manages to generate a label value covering array as the set of test cases to cover certain combinations of label values. Then, for each test case, LV-CIT relies on an object library to generate composite test images that perfectly match the specified labels, and reports classification errors if such labels cannot be correctly recognised. The experimental results on two popular datasets with six state-of-the-art image classifiers show that LV-CIT is more efficient than the existing CIT tools in generating label value covering arrays. LV-CIT is also effective in errors revelation, as it can find 111% more errors by using 20% fewer test images than the existing methods for testing multi-label image classifiers.

关键词： Correlation Image recognition Source coding Neural networks Closed box Libraries Reproducibility of results software reliability Testing Image classification

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Actor of Things: Resilient and Efficient Distributed AIoT Applications in an Actor System

Actor of Things: Resilient and Efficient Distributed AIoT Ap...

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Asia-Pacific Conference on software Engineering

作者： Tianqi Ren Chun Cao Jun Ma Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Complex and distributed AIoT applications usually involve multiple interconnected heterogeneous devices, leading to requirements of modeling device states and interaction diagrams. It is found that actor model is well-suited for distributed IoT contexts due to its inherent distributive nature and concurrency safety, but existing works lack runtime scalability and support for asynchronous task execution. In this paper, we propose Actor of Things, an approach to transform AIoT applications into efficient and resilient actor systems. We first transform synchronous function calls into Actor Task Graph which decouples nodes with real actors and executes asynchronously. The graph is then executed by Actor Runtime which deploys and schedules actors on IoT devices committed to lowest latency. Preliminary evaluations of our implementation demonstrate the advantages of proposed method in building distributed AIoT applications.

关键词： Concurrent computing Schedules Runtime software architecture Scalability Buildings Transforms Safety Internet of Things Context modeling

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DECOOP: Robust Prompt Tuning with Out-of-Distribution Detection 41

DECOOP: Robust Prompt Tuning with Out-of-Distribution Detect...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhi Yang, Ming Shi, Jiang-Xin Guo, Lan-Zhe Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Intelligence Science and Technology Nanjing University China

Vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot capabilities for various downstream *** performance can be further enhanced through few-shot prompt tuning ***, current studies evaluate the performance of learned prompts separately on base and new *** evaluation lacks practicality for real-world applications since downstream tasks cannot determine whether the data belongs to base or new classes in *** this paper, we explore a problem setting called Open-world Prompt Tuning (OPT), which involves tuning prompts on base classes and evaluating on a combination of base and new *** introducing Decomposed Prompt Tuning framework (DEPT), we theoretically demonstrate that OPT can be solved by incorporating out-of-distribution detection into prompt tuning, thereby enhancing the base-to-new *** on DEPT, we present a novel prompt tuning approach, namely, Decomposed Context Optimization (DECOOP), which introduces new-class detectors and sub-classifiers to further enhance the base-class and new-class *** results on 11 benchmark datasets validate the effectiveness of DEPT and demonstrate that DECOOP outperforms state-of-the-art methods, providing a significant 2% average accuracy improvement. Copyright 2024 by the author(s)

关键词： Visual languages

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Optimisation of sparse deep autoencoders for dynamic network embedding

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1361-1376页

作者： Huimei Tang Yutao Zhang Lijia Ma Qiuzhen Lin Liping Huang Jianqiang Li Maoguo Gong College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina Institute for Infocomm Research Agency for Science Technology and ResearchSingaporeSingapore Key Laboratory of Collaborative Intelligence Systems Ministry of EducationXidian UniversityXi'anChina

Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.

关键词： deep autoencoder dynamic networks low-dimensional feature space network embedding sparse structure

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