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UniXcoder: Unified Cross-Modal Pre-training for Code Representation

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arXiv 2022年

作者： Guo, Daya Lu, Shuai Duan, Nan Wang, Yanlin Zhou, Ming Yin, Jian School Of Computer Science And Engineering Sun Yat-sen University Guangdong Key Laboratory Of Big Data Analysis And Processing Guangzhou China Microsoft Research Asia Beijing China Langboat Technology Beijing China

Pre-trained models for programming languages have recently demonstrated great success on code intelligence. To support both code-related understanding and generation tasks, recent works attempt to pre-train unified encoder-decoder models. However, such encoder-decoder framework is sub-optimal for auto-regressive tasks, especially code completion that requires a decoder-only manner for efficient inference. In this paper, we present UniXcoder, a unified cross-modal pre-trained model for programming language. The model utilizes mask attention matrices with prefix adapters to control the behavior of the model and leverages cross-modal contents like AST and code comment to enhance code representation. To encode AST that is represented as a tree in parallel, we propose a one-to-one mapping method to transform AST in a sequence structure that retains all structural information from the tree. Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task. We evaluate UniXcoder on five code-related tasks over nine datasets. To further evaluate the performance of code fragment representation, we also construct a dataset for a new task, called zero-shot code-to-code search. Results show that our model achieves state-of-the-art performance on most tasks and analysis reveals that comment and AST can both enhance UniXcoder. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimal systems

High Sensitive Butanone Gas Sensor Based on Co3o4 Modified Sno2 Hollow Spheres with Ppb-Level Detection Limit

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SSRN

SSRN 2022年

作者： Yang, Liu Ji, Hanyang Yuan, Zhenyu Meng, Fanli College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China

Butanone is a precursor chemical and detrimental to human body, as a consequence, it is significant for social stability to accurately detect trace butanone. In this paper, Co3O4/SnO2 is prepared by modifying Co3O4 nanoparticles on the surface of SnO2 hollow spheres, demonstrating outstanding sensitivity to butanone. SEM, TEM, HRTEM, EDS, XRD, XPS, EPR and BET characterize the morphology, composition, and properties of Co3O4/SnO2. The response of 0.5 at% Co3O4/SnO2 to 100 ppm butanone is 3900 at 210 °C, moreover, the detection limit can reach ppb level (response is 7.2 to 1 ppb), with excellent repeatability and stability. Since Cox+ can replace Sn2+, the content of oxygen vacancy on the surface is adjusted by modifying Co3O4 with different content to improve the gas sensitivity. Of course, the specific surface area and pore size change correspondingly. The role of p-n heterojunction, as well as the synergistic sensitization principle of catalytic and small size effect, are also analyzed in detail. The gas sensing results show that 0.5 at% Co3O4/SnO2 is an excellent gas sensing material. © 2022, The Authors. All rights reserved.

关键词： Oxygen vacancies

Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

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arXiv 2022年

作者： Wu, Chao Ge, Wenhang Wu, Ancong Chang, Xiaobin School of Artificial Intelligence Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Computer Science and Engineering Sun Yat-sen University China

To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training samples could be unavailable under the ISolated Camera Supervised (ISCS) setting, e.g., a surveillance system deployed across distant scenes. To handle this challenging problem, a new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Specifically, the feature encoder and generator are end-to-end optimized under a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint learning procedure raises concern on the stability of generative model training. Therefore, a new feature generator, σ-Regularized Conditional Variational Autoencoder (σReg. CVAE), is proposed with theoretical and experimental analysis on its robustness. Extensive experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors. 1 © 2022, CC BY.

关键词： Cameras

UAV-Based Thermal Radiation Directionality Capture and Its Evaluation on Kernel-Driven Models

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Hu, Qingyang Yu, Wenping Zhu, Shenchao Huang, Yajun Zhang, Xi Liu, Meng Cao, Biao Southwest University Chongqing Engineering Research Center for Remote Sensing Big Data Application School of Geographical Sciences Chongqing400715 China Yibin Academy of Southwest University Yibin644000 China Jiaying University School of Geographic Sciences and Tourism Guangdong Meizhou514000 China Aerospace Information Research Institute Chinese Academy of Sciences State Key Laboratory of Remote Sensing Science Beijing100101 China

In order to correct the land surface temperature (LST) errors caused by angular effects to normalize the multisource data, various models have been proposed, especially the kernel-driven model shows good prospects for development. However, there are fewer studies on the validation of thermal radiation directionality (TRD) characteristics of features based on real measurements. In this article, a method based on unmanned aerial vehicle (UAV) observation of TRD characteristics of different surfaces is proposed to validate the TRD, which uses the circling flight mode to obtain the thermal radiation characteristics of features in multiple directions. Observations show that there is a significant hotspot effect in the three vegetation scenes, and the TRD and dispersion degree are different due to the differences in vegetation structure. For the realization of further applications of UAV-based methods for measuring TRD, the UAV-based angular observations are evaluated against the simulation results of the current kernel-driven model with high accuracy, and the results show that the correlation coefficients are all greater than 0.78, the coefficients of determination are greater than 0.60, and the root-mean-square error (RMSE) is less than 1. However, the ability of the model to extrapolate forward varies considerably in different vegetation scenes. The present study provides an effective and potential way to observe the TRD, which can lay a practical foundation for the theoretical study and model simulation of the TRD. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach

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arXiv 2023年

作者： Pan, Hongyang Liu, Yanheng Sun, Geng Wang, Pengfei Yuen, Chau College of Computer Science and Technology Jilin University Changchun130012 China Pillar Singapore University of Technology and Design Singapore487372 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

Unmanned aerial vehicles (UAVs)-aided device-to-device (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NP-hard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

An Overview on IRS-Enabled Sensing and Communications for 6G: Architectures, Fundamental Limits, and Joint Beamforming Designs

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arXiv 2024年

作者： Song, Xianxin Fang, Yuan Wang, Feng Ren, Zixiang Yu, Xianghao Zhang, Ye Liu, Fan Xu, Jie Ng, Derrick Wing Kwan Zhang, Rui Cui, Shuguang Guangdong518172 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Information Engineering Guangdong University of Technology Guangzhou510006 China Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Hefei230027 China Computer School Beijing Information Science and Technology University Beijing100096 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station’s (BS’s) non-line-of-sight (NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication (ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

Online Attentive Kernel-Based Temporal Difference Learning

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arXiv 2022年

作者： Yang, Guang Chen, Xingguo Yang, Shangdong Wang, Huihui Dong, Shaokang Gao, Yang The the Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University China The State Key Laboratory for Novel Software Technology Nanjing University China The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

A Solution-Based Tabu Search Algorithm for the Resource-Constrained Project Scheduling Problem with Step Deterioration

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Dai, Huafeng Li, Lei Mao, Run Liu, Xingyue Zhou, Kun School of Mechanical Engineering Chengdu University Chengdu610105 China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Chengdu610031 China CNPC National Engineering Research Center for Oil & Gas Drilling Equipment Co. Ltd. Chengdu610052 China School of Information Science and Engineering Chengdu University Chengdu610105 China Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm. © 2023 Huafeng Dai et al.

关键词： Deterioration

HOUGH-DCT Based Robust Watermarking for Medical Image 1

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12th International Symposium on Cyberspace Safety and Security, CSS 2020

作者： Mo, Ruiqi Liu, Jing Li, Jingbing Wang, Yulin Zhou, Jingjun School of Information and Communication Engineering Hainan University Haikou570228 China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou China

ISBN: (数字)9783030736712

ISBN: (纸本)9783030736705

The patients’ case files may be maliciously tampered with during network transmission. Once the patients’ medical record information is altered by criminals, serious medical accidents will occur. To solve the problem of information leakage, a robust medical image watermarking algorithm based on Hough transform and discrete cosine transform (HOUGH-DCT) is proposed. This method combines the feature vector of medical image, cryptography, hash function and zero watermark technology, and it makes up for the shortcoming that the traditional digital watermark method cannot protect the medical image. It has strong robustness and invisibility which can protect the patient's private information and medical image data security at the same time. Experimental results show that the algorithm can embed the watermark information at a faster speed without changing the medical image information and extract the watermark safely with better invisibility and robustness. © 2021, Springer Nature Switzerland AG.

关键词： Discrete cosine transforms