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Dataset Search over Integrated Metadata from China’s Public Data Open Platforms 1

11th CCF Big Data Conference, BigData 2023

作者： Chen, Qiaosheng Shi, Qing Cheng, Gong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

The aggregation, fusion, sharing, opening, development, and utilization of public data provide a solid basis for promoting the development of e-government and digital economy. These activities rely on an infrastructure software called public data open platform (PDOP) to provide enabling services. While China’s national PDOP has yet to be completed, one pathway is to integrate the existing hundreds of provincial-level and prefectural-level PDOPs. However, these local PDOPs exhibit high heterogeneity, e.g., using different data catalogs and different metadata formats. In this system paper, we meet the challenge by crawling and integrating metadata records for datasets registered in existing local PDOPs, and we develop a prototype PDOP that provides unified search services over the integrated metadata. We conduct experiments to evaluate the core components of our prototype. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Dataset search Metadata integration Open government data Public data open platform

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MG+: Towards Efficient Context Inconsistency Detection by Minimized Link Generation

MG+: Towards Efficient Context Inconsistency Detection by Mi...

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作者： Chen, Chuyang Wang, Huiyan Zhang, Lingyu Xu, Chang Yu, Ping State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science Nanjing University Jiangsu Nanjing China Software Institute Nanjing University Jiangsu Nanjing China

Self-adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location-aware navigation or resource-sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape-condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real-world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduc

关键词： constraint checking context inconsistency link redundancy

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Speech Recognition Method Based on Deep Learning of Artificial Intelligence: An example of BLSTM-CTC model 2023

Speech Recognition Method Based on Deep Learning of Artifici...

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5th International Symposium on Signal Processing Systems, SSPS 2023

作者： Chen, Kangyu Peng, Zhiyuan Department of Computer Science The University of Hong Kong Hong Kong State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798400700040

Under the influence of information, network and intelligent high-speed development situation, China's intelligent technology and other aspects have made great progress and achievements, derived a lot of advanced artificial intelligence technology, machine learning technology and deep learning technology, etc., to promote the development of intelligence and information in major fields. Artificial intelligence deep learning is the fusion of artificial intelligence technology and machine learning technology, which lays the foundation for the reform and innovation of artificial voice intelligent recognition technology and intelligent robot technology. So in order to improve the application level of intelligent speech recognition technology, it is necessary to continuously optimize the speech recognition method based on AI deep learning. In this regard, according to the relevant literature, this paper addresses the problem that phoneme features of varying duration are generated during the propagation of speech signals, and these features affect the correct rate of speech recognition, and the phoneme features of different lengths are standardized based on the deep learning research mentioned in this paper with BLSTM-CTC as an example. By evaluating the model on the Thchs30 and ST-CMDS datasets, the results show that the MCFN-based BLSTM-CTC speech recognition model has a reduced recognition word error rate compared with the traditional speech recognition model. © 2023 ACM.

关键词： Speech recognition

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Towards Better Dependency Scope Settings in Maven Projects 23

Towards Better Dependency Scope Settings in Maven Projects

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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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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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2023 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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