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14th Asia-Pacific Symposium on Internetware, Internetware 2023

作者： Yang, Haolin Chen, Lin Cao, Yulu Li, Yanhui Zhou, Yuming State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400708947

The emergence of build automation tools with dependency management features has significantly impacted software development. However, in the configuration process, improper settings of some configuration items, such as the dependency scope setting, may cause severe problems in the development process. Improper setting of dependency scope may cause problems such as missing dependencies and redundant dependencies, and may even spread the problem to the downstream of the software ecosystem. We conduct the first comprehensive empirical study of dependency scope settings in Maven projects to investigate the current state of dependency scope settings. We collect 5,433 commits from 65 popular open-source projects on GitHub, including 20,076 dependency scope settings. We also manually analyze 124 improper scope setting issues sampled from 2,609 Java projects. By analyzing these data, we reveal the typical symptoms and root causes of problems caused by improper dependency scope settings, and summarize 5 patterns of dependency scope modification. We provide suggestions for developers to better set and manage the dependency scope, and provide some ideas and experiences for the development of tools related to dependency scope setting. © 2023 ACM.

关键词： software design

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Enhancing In-Context Learning with Answer Feedback for Multi-span Question Answering 12th

Enhancing In-Context Learning with Answer Feedback for Mul...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Huang, Zixian Zhou, Jiaying Xiao, Gengyang Cheng, Gong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783031446955

Whereas the emergence of large language models (LLMs) like ChatGPT has exhibited impressive general performance, it still has a large gap with fully-supervised models on specific tasks such as multi-span question answering. Previous researches found that in-context learning is an effective approach to exploiting LLM, by using a few task-related labeled data as demonstration examples to construct a few-shot prompt for answering new questions. A popular implementation is to concatenate a few questions and their correct answers through simple templates, informing LLM of the desired output. In this paper, we propose a novel way of employing labeled data such that it also informs LLM of some undesired output, by extending demonstration examples with feedback about answers predicted by an off-the-shelf model, e.g., correct, incorrect, or incomplete. Experiments on three multi-span question answering datasets as well as a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM’s in-context learning performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： ChatGPT In-context learning Multi-span question answering

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DyFuzz: Skeleton-based Fuzzing for Python Libraries 23

DyFuzz: Skeleton-based Fuzzing for Python Libraries

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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Xia, Xinmeng Feng, Yang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350319583

Programming libraries are indispensable for programming languages. Programmers can access the pre-written codes in these libraries via the application programmable interfaces (API), optimizing and accelerating their programming tasks. However, defects in these libraries may cause unexpected software behaviors, threatening their robustness and safety. Thus, it is crucial to ensure the quality of the libraries. This paper explores an alternative approach, namely Fuzzing Skeleton API (FSA), for detecting library bugs in Python. For the given API, FSA aims to generate massive inputs, i.e., different argument combinations, and pass them to the API to verify its correctness and reliability. To realize this, FSA first abstracts the API into a skeleton by modeling its usage of parameters as placeholders. Then, it can generate the seed API calls by filling these placeholders with pre-defined arguments. Finally, the approach incorporates four mutation strategies, i.e., bit mutation, literal mutation, element mutation, and attribute mutation, to mutate different arguments and hence generate massive API calls. We have implemented the proposed approach into an automated tool, namely DyFuzz, for testing Python libraries. In less than one month of the fuzzing experiment, DyFuzz detected 14 library bugs, of which nine have been confirmed as unknown bugs. © 2023 IEEE.

关键词： Python

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Adaptive Collaborative Air-Ground Edge Caching in Dynamic Vehicular Network Scenarios 11

Adaptive Collaborative Air-Ground Edge Caching in Dynamic Ve...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Luo, Fei Wang, Junhua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350314014

Mobile edge caching is a promising technique for supporting the applications of Intelligent Transportation Systems (ITS), which involve huge volumes of image and video contents. Specifically, critical challenges arise in meeting the contents requirements of moving vehicles. In this work, we investigate the collaborative contents delivery in air-ground integrated vehicular networks, where unmanned aerial vehicles (UAVs) and roadside units (RSUs) cache popular contents and further deliver it to nearby vehicles. Efficient contents placement is crucial because of the high mobility of vehicles and UAVs, and the limited caching capacity of edge nodes. To address this, we formulate the heterogeneous cache optimization problem as a generalized multi-dimensional multiple knapsack problem (MMKP), which is NP-hard. In particular, we propose an adaptive collaborative caching optimization (ACCO) algorithm that employs the Kernel Search (KS) framework to find a near-optimal caching scheme, with dynamically adjustable dimensions and solution time for subproblems. Furthermore, we use the k-means clustering algorithm to categorize contents based on service latency, caching cost, and contents size, adding a greedy-based load balancing strategy to achieve cost-effective optimal matching of contents and edge nodes. Comprehensive simulations verify the effectiveness of the proposed cooperative edge caching architecture and the ACCO algorithm in reducing service delay and caching costs for contents delivery. © 2023 IEEE.

关键词： Cost effectiveness

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AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

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Journal of computer science & technology 2024年第2期39卷 401-420页

作者：龚成卢冶代素蓉邓倩杜承昆李涛 College of Software Nankai UniversityTianjin 300350China College of Computer Science Nankai UniversityTianjin 300350China State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and *** exploration implies heavy workloads for domain experts,and an automatic compression method is ***,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real *** this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human *** can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the *** is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our *** optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory *** is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network *** have implemented AutoQNN and integrated it into *** experiments demonstrate that AutoQNN can consistently outperform state-of-the-art *** 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art ***,c

关键词： automatic quantization mixed precision quantizing scheme search quantizing precision learning quan-tized architecture generation

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Understanding Bugs in Rust Compilers 23

Understanding Bugs in Rust Compilers

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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Xia, Xinmeng Feng, Yang Shi, Qingkai Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350319583

Rust compilers play a foundational role in the Rust language. Like any complex system, they are susceptible to bugs, which can impact the correctness and reliability of the compiled Rust programs. To gain a deeper understanding of these bugs, this paper presents the first comprehensive analysis of historical bugs in two widely used Rust compilers: Rustc and Rust-GCC. The analysis delves into the bugs' characteristics, bug-proneness locations, bug root causes, and bug-fixing efforts. The findings reveal that the majority of bugs in Rustc are associated with the compiler's kernel, while Rust-GCC experiences most bugs related to the cleanup process. Among all modules, the 'src/librustc' module exhibits the highest bug-proneness in the Rustc compiler, whereas the 'gcc/rust' modules demonstrate the highest bug-proneness in the Rust-GCC compiler. Furthermore, the study reveals that the bug-fixing process is accelerated when test cases utilize Rust's concurrency features. © 2023 IEEE.

关键词： Program compilers

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Learning Robust Multi-Modal Representation for Multi-Label Emotion Recognition via Adversarial Masking and Perturbation 23

Learning Robust Multi-Modal Representation for Multi-Label E...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Ge, Shiping Jiang, Zhiwei Cheng, Zifeng Wang, Cong Yin, Yafeng Gu, Qing State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781450394161

Recognizing emotions from multi-modal data is an emotion recognition task that requires strong multi-modal representation ability. The general approach to this task is to naturally train the representation model on training data without intervention. However, such natural training scheme is prone to modality bias of representation (i.e., tending to over-encode some informative modalities while neglecting other modalities) and data bias of training (i.e., tending to overfit training data). These biases may lead to instability (e.g., performing poorly when the neglected modality is dominant for recognition) and weak generalization (e.g., performing poorly when unseen data is inconsistent with overfitted data) of the model on unseen data. To address these problems, this paper presents two adversarial training strategies to learn more robust multi-modal representation for multi-label emotion recognition. Firstly, we propose an adversarial temporal masking strategy, which can enhance the encoding of other modalities by masking the most emotion-related temporal units (e.g., words for text or frames for video) of the informative modality. Secondly, we propose an adversarial parameter perturbation strategy, which can enhance the generalization of the model by adding the adversarial perturbation to the parameters of model. Both strategies boost model performance on the benchmark MMER datasets CMU-MOSEI and NEMu. Experimental results demonstrate the effectiveness of the proposed method compared with the previous state-of-the-art method. Code will be released at https://***/ShipingGe/MMER. © 2023 ACM.

关键词： Emotion Recognition

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When CPN Meets AI: Resource Provisioning for Inference Query upon Computing Power Network 29

When CPN Meets AI: Resource Provisioning for Inference Query...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Ji, Mingtao Qian, Zhuzhong Ye, Baoliu Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350330717

Performing machine learning inference at the network edge, named Edge Inference, showing benefits like low latency, reduced data traffic, and improved user privacy, has attracted massive attention. Computing Power Network (CPN) creates opportunities for edge inference, but also poses multiple challenges for service providers, including computing power selections from different enterprises, the time-coupled decision for resource provisioning and the unpredictable CPN network status. To overcome these challenges, this study formulates a time-varying integer program problem whose goal is to minimize long-term costs, involving switching costs, operational costs, communication costs, and queuing costs. Then we design a group of polynomial-time online algorithms to make online decisions, by taking into account stochastic inputs. Our algorithms adaptively make control decisions by solving delicately constructed subproblems based on the inputs predicted via online learning. Specifically, we first obtain fractional solutions which are transformed into integers for deployment with expectations preserving. Furthermore, we conduct a rigorous proof and establish the competitive ratio which highlights the difference between the performance of our proposed algorithms and the offline optimum. Our comprehensive evaluations, using datasets from real systems, demonstrate that our algorithms outperform multiple alternatives, up to an average of 35% cost reduction, confirming the effectiveness of our algorithms. © 2023 IEEE.

关键词： Computing Power Network Resource Provisioning

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TalkingStyle: Personalized Speech-Driven 3D Facial Animation with Style Preservation

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 1-12页

作者： Song, Wenfeng Wang, Xuan Zheng, Shi Li, Shuai Hao, Aimin Hou, Xia Computer School Beijing Information Science and Technology University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

It is a challenging task to create realistic 3D avatars that accurately replicate individuals' speech and unique talking styles for speech-driven facial animation. Existing techniques have made remarkable progress but still struggle to achieve lifelike mimicry. This paper proposes “TalkingStyle”, a novel method to generate personalized talking avatars while retaining the talking style of the person. Our approach uses a set of audio and animation samples from an individual to create new facial animations that closely resemble their specific talking style, synchronized with speech. We disentangle the style codes from the motion patterns, allowing our method to associate a distinct identifier with each person. To manage each aspect effectively, we employ three separate encoders for style, speech, and motion, ensuring the preservation of the original style while maintaining consistent motion in our stylized talking avatars. Additionally, we propose a new style-conditioned transformer decoder, offering greater flexibility and control over the facial avatar styles. We comprehensively evaluate TalkingStyle through qualitative and quantitative assessments, as well as user studies demonstrating its superior realism and lip synchronization accuracy compared to current state-of-the-art methods. To promote transparency and further advancements in the field, we also make the source code publicly available at https://***/wangxuanx/TalkingStyle. IEEE

关键词： Synchronization

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Bi-Directional and Triangular Circulation Fusion Neural Networks for Small Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期35卷 5140-5152页

作者： Li, Fangyu Duan, Junzhu Zhang, Qiyu Shan, Caifeng Han, Honggui the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute Beijing University of Technology Beijing100124 China the State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China the School of Intelligence Science and Technology Nanjing University Suzhou215163 China

Deep learning-driven object detection models are capable of accurately identifying and localizing objects. However, small objects contain limited information relative to global features, resulting in the fact that detection models often do not learn small object features adequately. To enhance the precision in detecting small objects, we propose a bi-directional and triangular circulation fusion neural network (BTFN). First, to selectively strengthen the position features of small objects, we propose a feature circulation extraction module composed of a bi-directional triangular densely nested convolutional network (BTF), thus achieving repetitive multi-layer feature fusion. Second, to fill up the semantic gaps between different scales of features, we design a mixed dual attention module (MDA) in the bi-directional triangular densely nested network. Third, to mitigate the lost information in the neural networks with deep layers as well as improve the inference time, we design a re-parameterization bi-directional composite feature fusion module (Rep-BFM) that fuses the features of multiple scales. The proposed model is evaluated extensively on the MS COCO, Tsinghua-Tencent 100k, and Haier dismantled parts of used home appliances datasets. The experiment results show that the proposed model improves the AP on MS COCO by 4%, especially the APS of small objects is improved by 7.7% compared with SOTA models. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Domestic appliances

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