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12th International Conference on Mobile Networks and Management, MONAMI 2022

作者： Wang, Hanmeng Peng, Kai Zhao, Bohai Huaqiao University Quanzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783031324420

The Internet of Things (IoT) enables all devices to sense, communicate, which has given rise to the evolution of traditional educational planning. Benefiting from this revolution, a new paradigm, named smart campus came into being. More specifically, teaching way has changed significantly with the help of quite a few emerging real-time applications. However, the computing capabilities of smart devices (SDs) are constrained. Fortunately, mobile edge computing (MEC) has emerged as a scalable computing model to augment capacity of SDs by placing computing resources closer to users. However, the resources of the ESs in MEC are not inexhaustible. In view of this, we investigate computation offloading for latency-sensitive application which is modeled as workflow application in MEC-enabled smart campuses. Specifically, time consumption, energy consumption, and resource utilization are regarded as our optimized goals. To this end, a corresponding method is proposed. Extensive experimental results show that our method can achieve effective optimization effect in terms of given objectives. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Multiobjective optimization

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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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Engineer Insights: The Challenges of Implementing Agile at Scale in software Development 35

Engineer Insights: The Challenges of Implementing Agile at S...

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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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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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SCARE: A novel Framework to Enhance Chinese Harmful Memes Detection

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IEEE Transactions on Affective Computing 2024年第2期16卷 933-945页

作者： Gu, Tianlong Feng, Mingfeng Feng, Xuan Wang, Xuemin Jinan University College of Information Science and Technology College of Cyber Security Guangzhou China Guilin University of Electronic Science and Technology College of Computer and Information Security Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin541004 China

Harmful meme detection presents a significant multimodal challenge that necessitates contextual background knowledge and comprehensive inference. Although some research studies have been related to harmful meme detection in English, detecting harmful memes in Chinese is also an unresolved issue. In this paper, to bridge this gap, we constructed a Chinese harmful meme detection dataset, named CHMEMES. Furthermore, existing multimodal alignment methods have shown poor performance in tasks involving harmful meme detection, where there is a mismatch between the image and text components. To improve the task, we propose a multimodal framework Semantic Contrastive Alignment fRamEwork (SCARE), which enables fully representing both cross-modal and intra-modal information. For cross-modal information, we introduce a crossmodal contrast alignment objective to maximize the mutual information between image and text. For intra-modal information, we design a new intra-modal contrast objective to achieve more robust visual and textual representation learning. Moreover, we present a simple yet efficient vision prompt tuning paradigm for parameter-efficient harmful meme detection. We conduct extensive experiments on the constructed Chinese dataset and the existing English dataset. Experimental results show that our method outperforms state-of-the-art baselines in harmful meme detection. © 2010-2012 IEEE.

关键词： Semantics

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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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