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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Wenjie Xu Lin Chen Chenghao Su Yimeng Guo Yanhui Li Yuming Zhou Baowen Xu State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China

Python has become increasingly popular and widely used in many fields. Dynamic features of Python provide much convenience for developers. However, they can also cause many type-related bugs undetected until runtime, which increases the cost of maintenance. Static type checking is essential to find bugs early, and the introduction of gradual typing and type annotations makes it easier to perform static type analysis. However, it remains to be investigated how well gradual typing improves real bug detection. Therefore, we conducted a comprehensive study on three widely used checkers: MyPy, PyRight, and PyType. We used a benchmark containing 10 popular Python projects with 40 real type-related bugs. First, we performed static type checking on the projects with and without type annotations to evaluate the effectiveness of finding real bugs. Second, we manually analyzed the missing bugs and investigated the reasons. The results show that the three tools can detect 29 of the 40 studied bugs after annotating, while only 14 bugs are detected before annotating. We also found that type annotations can substantially improve the ability of static type checkers to detect real bugs. A detailed analysis of bugs missed by the checkers shows that: (i) the accuracy of type analysis is challenged when it comes to programs with complicated dynamic features, such as dynamically changing object’s attributes, even with annotations; (ii) the inaccurate type annotations can undermine the ability of static type checkers to detect real bugs; (iii) static type checkers have different checking strategies in some cases, which has an impact on real bug detection. Our study can not only enable developers to better understand static type checking and make better use of them but also guide future research.

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Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Seed Feature Maps-based CNN Models for LEO Satellite Remote ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Lu, Zhichao Ding, Chuntao Wang, Shangguang Cheng, Ran Juefei-Xu, Felix Boddeti, Vishnu Naresh Sun Yat-sen University School of Software Engineering Zhuhai China Beijing Jiaotong University School of Computer and Information Technology Beijing China Beijing University of Posts and Telecommunications Key Laboratory of Networking and Switching Technology Beijing China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China New York University New YorkNY United States Michigan State University Department of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350304855

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3 × fewer parameters and 2.2 × fewer FLOPs. © 2023 IEEE.

关键词： Neural network models

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AMSC: Adaptive Multi-channel Graph Convolutional Network-Enhanced Web Services Classification 23

AMSC: Adaptive Multi-channel Graph Convolutional Network-Enh...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Qing, Yueying Cao, Buqing Peng, Mi Zhang, Lulu Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the development of Service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81%, 19.35%, 19.16%, 11.56%, and 7.75% respectively. © 2021 IEEE.

关键词： Correlation Adaptive systems Network topology Smart cities Quality of service Feature extraction Topology

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A Novel Method for Precipitation Nowcasting Based on ST-LSTM

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computers, Materials & Continua 2022年第9期72卷 4867-4877页

作者： Wei Fang Liang Shen Victor S.Sheng Qiongying Xue School of Computer&Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China Department of Computer Texas Tech UniversityLubbockTX79409USA

Precipitation nowcasting is of great significance for severe convective weather *** echo extrapolation is a commonly used precipitation nowcasting ***,the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation *** reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data *** order to solve the above problems,we select the spatiotemporal long short-term memory(ST-LSTM)as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model’s ability to capture short-term motion information which plays a vital role in the prediction of radar echo motion *** the purpose of enhancing the model’s ability to retain long-term important information,we also introduce the channel attention mechanism to achieve this *** the experiment,the training and testing datasets are constructed using radar data of Shanghai,we compare our model with three benchmark models under the reflectance thresholds of 15 and *** results demonstrate that the proposed model outperforms the three benchmark models in radar echo extrapolation task,which obtains a higher accuracy rate and improves the clarity of the extrapolated image.

关键词： Precipitation nowcasting radar echo extrapolation ST-LSTM attention mechanism

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Neural Machine Translation by Fusing key Information of Text

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computers, Materials & Continua 2023年第2期74卷 2803-2815页

作者： Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Science and Technology on Altitude Simulation Laboratory Sichuan Gas Turbine Establishment Aero Engine Corporation of ChinaMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China School of Computer Science Southwest Petroleum UniversityChengdu610500China Department of Chemistry Physics and Atmospheric SciencesJackson State UniversityJacksonMS 39217USA

When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that *** the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long *** the other hand,the most important part in traditional translation tasks is the translation of key *** the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be *** order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on *** model proposed in this paper extracts the keywords of the source language text separately as the input of the *** the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the *** incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very *** order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification *** experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test *** experimental results show the advantages of the model proposed in this paper.

关键词： key information transformer fusion neural machine translation

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Deep Learning-Based Joint Channel Estimation and Implicit CSI Feedback 11th

Deep Learning-Based Joint Channel Estimation and Implicit C...

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11th China Conference on Command and Control, C2 2023

作者： Wan, Liangtian He, Jifeng Liu, Kaihui Sun, Lu Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan523820 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9789819990207

Previous studies often address channel estimation and channel state information (CSI) implicit feedback issues in 5G new radio (NR) system independently, posing a challenge to achieve effective coordination between the two processes in a shared environment. Consequently, we propose a deep learning-based scheme that jointly addresses channel estimation and implicit CSI feedback. In this scheme, the received pilot information from user equipment (UE) is regarded as a two-dimensional image. Channel estimation is performed employing the ResMLP architecture, whereas the Transformer architecture is utilized for implicit CSI feedback. Furthermore, a novel training approach is introduced to further enhance the performance of the proposed scheme. Simulation results illustrate a substantial improvement compared to the reference scheme employed in the 3rd Wireless Communication Artificial Intelligence (AI) Competition (WAIC). © Chinese Institute of Command and Control 2024.

关键词： Channel estimation

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Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

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IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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ExpertDRL: Request Dispatching and Instance Configuration for Serverless Edge Inference with Foundation Models

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IEEE Transactions on Mobile Computing 2025年

作者： Zeng, Yue Zhou, Junlong Ye, Baoliu Qu, Zhihao Guo, Song Gong, Tianjian Li, Pan Nanjing University of Science and Technology Department of Computer Science and Engineering Jiangsu Nanjing210094 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China Hong Kong University of Science and Technology Hong Kong Southwest University College of Electronic and Information Engineering Chongqing400715 China

The prevalence of the pre-training & fine-tuning paradigm enables machine learning models to quickly adapt to various downstream tasks by fine-tuning pre-trained foundation models (FMs), greatly facilitating various IoT applications that rely on model inference in dynamic edge serverless environments. Efficiently dispatching inference requests and configuring instances to batch inference requests can significantly enhance resource efficiency. However, existing serverless inference solutions are tailored for traditional models, make coarse-grained request dispatching and instance configuration decisions, fail to exploit the shared model backbone characteristics of the FM and capture delayed rewards in dynamic environments, and ignore communication latency between edge sites, resulting in high costs and constraint violations. In this paper, we leverage our insight that fine-grained batch inference requests can effectively exploit the shared model backbone feature of FM to save monetary costs. We propose an algorithm that incorporates deep reinforcement learning (DRL) and expert intervention for fine-grained request dispatching and instance configuration, where the DRL component outputs fractional solutions as guidance, while the expert intervention module integrates our insights—batching reduces monetary costs at the expense of increased inference latency, whereas higher configurations shorten inference latency. This module rounds fractional solutions and adjusts instance configurations to search for optimal solutions while satisfying constraints, with theoretical guarantees rigorously proved. Finally, we conducted our experiments on an OpenFaas-based platform and simulator, and extensive trace-driven evaluation results show that ExpertDRL can save costs by up to 85.14% and improve request acceptance ratio by up to 26.93%, compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning

InfLoRA: Interference-Free Low-Rank Adaptation for Continual...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yan-Shuo Liang Wu-Jun Li Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University P. R. China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently, parameter-efficient finetuning (PEFT), which involves freezing a pretrained model and injecting a small number of learnable parameters to adapt to downstream tasks, has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT, most of them do not consider how to eliminate the interference of the new task on the old tasks, which inhibits the model from making a good tradeoff between stability and plasticity. In this work, we propose a new PEFT method, called interference-free low-rank adaptation (InfLoRA), for continual learning. InfLoRA injects a small number of parameters to reparameterize the pretrained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pretrained weights within a subspace. Furthermore, InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks, making a good tradeoff between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets. Code is available at https://***/liangyanshuo/InfLoRA.

关键词： Continuing education Adaptation models computer vision Codes Interference Stability analysis Pattern recognition

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Controllable Financial Market Generation with Diffusion Guided Meta Agent

arXiv

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arXiv 2024年

作者： Huang, Yu-Hao Xu, Chang Liu, Yang Liu, Weiqing Li, Wu-Jun Bian, Jiang National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China Microsoft Research Asia China

Order flow modeling stands as the most fundamental and essential financial task, as orders embody the minimal unit within a financial market. However, current approaches often result in unsatisfactory fidelity in generating order flow, and their generation lacks controllability, thereby limiting their application scenario. In this paper, we advocate incorporating controllability into the market generation process, and propose a Diffusion Guided meta Agent (DiGA) model to address the problem. Specifically, we utilize a diffusion model to capture dynamics of market state represented by time-evolving distribution parameters about mid-price return rate and order arrival rate, and define a meta agent with financial economic priors to generate orders from the corresponding distributions. Extensive experimental results demonstrate that our method exhibits outstanding controllability and fidelity in generation. Furthermore, we validate DiGA’s effectiveness as generative environment for downstream financial applications. © 2024, CC BY.

关键词： Commerce

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