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arXiv 2022年

作者： Gu, Yu Pahuja, Vardaan Cheng, Gong Su, Yu Department of Computer Science and Engineering The Ohio State University United States State Key Laboratory for Novel Software Technology Nanjing University China

Recent advances in deep learning have greatly propelled the research on semantic parsing. Improvement has since been made in many downstream tasks, including natural language interface to web APIs, text-to-SQL generation, among others. However, despite the close connection shared with these tasks, research on question answering over knowledge bases (KBQA) has comparatively been progressing slowly. We identify and attribute this to two unique challenges of KBQA, schema-level complexity and fact-level complexity. In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges. Regardless of the unique challenges, we argue that we can still take much inspiration from the literature of semantic parsing, which has been overlooked by existing research on KBQA. Based on our discussion, we can better understand the bottleneck of current KBQA research and shed light on promising directions for KBQA to keep up with the literature of semantic parsing, particularly in the era of pre-trained language models. © 2022, CC BY.

关键词： Deep learning

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STAR-RIS and UAV Combination for Simultaneous Task Offloading and Communications in MEC Networks 16

STAR-RIS and UAV Combination for Simultaneous Task Offloadin...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom Suzhou215163 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China

ISBN: (纸本)9798350390643

This study investigates a simultaneous tasks of-floading and communications (STOC) strategy in mobile edge computing (MEC) networks, facilitated by the integration of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and unmanned aerial vehicles (UAVs). Diverging from conventional MEC approaches, the newly proposed scheme simultaneously accounts for the computational and communicative capacities of MEC networks, rendering it a more pragmatic solution in practice. An optimization problem is devised to maximize the weighted sum of the minimum offloaded task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. A novel alternating optimization method is proposed to solve this non-convex problem with strong couplings among variables. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme. © 2024 IEEE.

关键词： Resource allocation

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On the noise robustness of in-context learning for text generation 24

On the noise robustness of in-context learning for text gene...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hongfu Gao Feipeng Zhang Wenyu Jiang Jun Shu Feng Zheng Hongxin Wei Department of Statistics and Data Science Southern University of Science and Technology and School of Economics and Finance Xi'an Jiaotong University School of Economics and Finance Xi'an Jiaotong University Department of Statistics and Data Science Southern University of Science and Technology and National Key Laboratory for Novel Software Technology Nanjing University School of Mathematics and Statistics Xi'an Jiaotong University Department of Computer Science and Engineering Southern University of Science and Technology Department of Statistics and Data Science Southern University of Science and Technology

ISBN: (纸本)9798331314385

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking

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Risk Region-based Prediction Model for the Epidemic Spreading

Risk Region-based Prediction Model for the Epidemic Spreadin...

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International Conference on computer Supported Cooperative Work in Design

作者： Fuwei Li Dianjie Lu Baoxu An Guijuan Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Studying the spread process of epidemics in the crowd is a significant topic, which can help to take measures in advance to avoid epidemic spread and reduce the losses of life and property of the general public. However, this is a challenging problem that how to capture the spatio-temporal characteristics of the epidemic spreading. In this paper, we propose a risk region-based prediction model to characterize the spread feature. First, we propose a risk region-based spread model to describe the spatial relationship between individuals by dividing risk regions and assigning different spread characteristics to different risk regions. Second, we develop a spatio-temporal point process-based infection intensity quantification method to describe the variation of epidemic spread characteristics over time in different risk regions in the form of intensity. Then, we propose a long short-term memory (LSTM) based intensity function prediction method to solve the corresponding intensity and predict the spread process by the spatio-temporal point process (STPP). Finally, we implement an epidemic spreading simulation platform to verify our method and visualize the experimental results. The proposed model is expected to provide guidance for predicting the spread of epidemics.

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CDGNet: A Cross-Time Dynamic Graph-Based Deep Learning Model for Vehicle-Based Traffic Speed Forecasting

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Yuchen Luo, Haiyong Zhao, Fang Sun, Poly Z.H. Qin, Yanjun Zeng, Liang Hui, Bo Wang, Chenxing School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Industrial Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Tsinghua University Beijing China University of Tulsa Tulsa USA

Vehicle-based traffic speed forecasting aims to predict the average speed of vehicles on the road in the future, which is an essential side information in intelligent vehicles and beneficial to safe autonomous driving, yet is very challenging due to the complex and dynamic spatio-temporal dependencies in real-world vehicle-based traffic. To extract intricate correlations among multiple vehicle-based speed time series, previous methods have used graph convolution networks. However, the conventional static and dynamic graphs fail to reflect the traffic evolution and the hysteresis spatial influence caused by vehicle movement. To address this issue, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for vehicle-based traffic speed forecasting. The model is able to effectively capture hysteresis spatial dependencies between each time slice and its historical time slices through the cross-time dynamic graph-based GCN. Meanwhile, a gating mechanism is integrated into our cross-time dynamic graph, which conforms to the sparse correlation in the real world. Besides, GCNs are incorporated into a novel encoder-decoder architecture to forecast multi-step speed. Experimental results on three real-world vehicle-based traffic speed datasets demonstrate the superiority of our CDGNet over various state-of-the-art spatio-temporal forecasting methods and the effectiveness of each component. We additionally provide a visualization of our cross-time dynamic graph to show the capability of assisting intelligent vehicles to avoid congestion. IEEE

关键词： Forecasting

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Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images

Dual Cross-Stage Partial Learning for Detecting Objects in D...

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IEEE International Conference on Data Mining (ICDM)

作者： Jinbiao Zhao Zhao Zhang Jiahuan Ren Haijun Zhang Zhongqiu Zhao Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Department of Computer Science Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP.

关键词： Accuracy Quantization (signal) Codes Data integrity Training data Object detection Feature extraction Image restoration Calibration Data mining

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model

Emergency Transportation and Structural Prediction and Optim...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： Yiyang Nie Xiang Chen Xuanli Wu Zihao Deng School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China Shcool of Mathematics and Computational Science Hunan University of Science and Technology Xiangtan China

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model; second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed; finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes.

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CI-Based Block-Level Precoding for ISAC System 16

CI-Based Block-Level Precoding for ISAC System

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wang, Yiran Hu, Xiaoyan Li, Ang Wong, Kai-Kit Yang, Kun School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom Suzhou215163 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China

ISBN: (纸本)9798350390643

In this paper, we propose a constructive interference (CI)-based precoding scheme and extend the proposed precoding scheme from the symbol level to the block level for multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems. The illumination power of radar is maximized under communication CI constraint. Since the optimization objective function is not convex, we use succesive convex ap-proximation (SCA) method to solve the proposed optimization problem. Furthermore, we formulate the dual problem of the convex subproblem in SCA method. It is a simple quadratic programming (QP) problem and can be efficiently solved. The simulation results show that the proposed CI-based precoding design has significant advantages in enhancing the ISAC performance with acceptable computational complexity. © 2024 IEEE.

关键词： Convex optimization

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Deep reinforcement learning-based panic crowd evacuation simulation

Deep reinforcement learning-based panic crowd evacuation sim...

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International Conference on computer Supported Cooperative Work in Design

作者： Baoxu An Guijuan Zhang Fuwei Li Nianyun Song Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Crowd evacuation simulation can provide guidance for public emergencies or mass casualties which is easy to cause crowding and stampede. Deep reinforcement learning is an effective method for the path planning of crowd evacuation simulation which can reduce the dependence on data and has strong generalization. However, current methods of deep reinforcement learning ignore the consideration of panic emotion which could result in weak authenticity since the panic emotion has a significant impact on crowd evacuation. To address this problem, we propose a novel deep reinforcement learning for the panic crowd evacuation. First, a quadrant based crowd network is constructed to guide the movement of agents according to the change of panic degree in different quadrants. Second, a method of quantifying panic degree is proposed. Through the mean field equation, the dynamic evolution of agent states is realized, and the panic degree in different regions is quantified. Finally, a panic crowd evacuation model based on multi-agent deep deterministic policy gradient (MADDPG) was established. The computated panic degree is introduced into the reward function to recommend a path for multiagent to avoid panic. Experimental results show the effectiveness of our method.

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Comprehensive Semantic Repair of Obsolete GUI Test Scripts for Mobile Applications

Comprehensive Semantic Repair of Obsolete GUI Test Scripts f...

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International Conference on software Engineering (ICSE)

作者： Shaoheng Cao Minxue Pan Yu Pei Wenhua Yang Tian Zhang Linzhang Wang Xuandong Li State Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Graphical User Interface (GUI) testing is one of the primary approaches for testing mobile apps. Test scripts serve as the main carrier of GUI testing, yet they are prone to obsolescence when the GUIs change with the apps' evolution. Existing repair approaches based on GUI layouts or images prove effective when the GUI changes between the base and updated versions are minor, however, they may struggle with substantial changes. In this paper, a novel approach named COSER is introduced as a solution to re-pairing broken scripts, which is capable of addressing larger GUI changes compared to existing methods. COSER incorporates both external semantic information from the GUI elements and internal semantic information from the source code to provide a unique and comprehensive solution. The efficacy of COSER was demonstrated through experiments conducted on 20 Android apps, resulting in superior performance when compared to the state-of-the-art tools METER and GUIDER. In addition, a tool that implements the COSER approach is available for practical use and future research.

关键词： Meters Source coding Semantics Layout Maintenance engineering Aging Mobile applications

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