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Asia-Pacific Conference on Software Engineering

作者： Guang Yang Yanlin Zhou Xiang Chen Chi Yu School of Information Science and Technology Nantong University China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China

ISBN: (纸本)9781665437851

Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo.

关键词： Codes Semantics Natural languages Writing Feature extraction Transformers Generators

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Insulator detection method based on multi-angle region proposal network

Insulator detection method based on multi-angle region propo...

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第三十九届中国控制会议

作者： Zhihao Chen Yewei Xiao Yan Zhou School of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University

Aiming at the direction of insulators are uncertain in the inspection images of transmission lines, and the problems of missed detection and inaccurate positioning due to complex backgrounds and occlusions, a method of insulators detection based on multi-angle region proposal network is proposed. Firstly, using a convolutional neural network, that characteristics of the different levels of features are obtained, and a feature fusion of shallow features and in-depth features is used to obtain richer features. Next, using the region proposal network based on Faster R-CNN, multi-angle factors are added and fine-tuned by non-maximum suppression so that it can generate candidate frames in any orientation to fit the target better. At the same time, the original pooling layer was changed to the RoIAlign layer. Finally, a 1 × 1 convolution layer is added before the fully connected layer of the network in order to reduce the parameters of its feature map and avoid overfitting. The inspection insulator image dataset is constructed and tested. The experimental results show that the final detection result obtained using this method can accurately detect the insulator, and its performance is better than the existing mainstream target detection algorithms and has high engineering application value.

关键词： Insulator Faster R-CNN multi-angle region proposal network RoIAlign

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

arXiv

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Global and Local Feasible Solution Search for Solving Constrained Multi-Objective Optimization

SSRN

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SSRN 2022年

作者： Huang, Weixiong Zou, Juan Liu, Yuan Yang, Shengxiang Zheng, Jinhua Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China School of Computer Science School of Cyberspace Science University of Xiangtan Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Obtaining a set of effective solutions for constrained multi-objective optimization problems (CMOPs) is a critical challenge since constraints may make the feasible regions of optimization problems complex. This paper proposes a constrained multi-objective evolutionary algorithms framework based on global and local feasible solutions search to address this issue. The proposed framework is divided into three stages, and an adaptive method is proposed to decide when to switch the search state. In the first stage, the framework ignores constraints and searches for feasible solutions by the global search operator. After processing the first stage, the rough range of the feasible solutions in the decision space is determined. In the second stage, ignoring constraints, the framework searches feasible solutions in the range determined by the first stage through the local search operator to explore small feasible regions that are hard to find. Finally, in the third stage, the framework reuses these excellent feasible solutions information to guide population evolution while considering constraints. The proposed framework has been compared with four state-of-the-art constrained multi-objective algorithms on four benchmark suites and three real-world applications. The complete experimental results show that the proposed framework has high competitiveness for solving CMOPs. © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

arXiv

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arXiv 2023年

作者： Lin, Xuan Dai, Lichang Zhou, Yafang Yu, Zu-Guo Zhang, Wen Shi, Jian-Yu Cao, Dong-Sheng Zeng, Li Chen, Haowen Song, Bosheng Yu, Philip S. Zeng, Xiangxiang Xiangtan China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China College of Informatics Huazhong Agricultural University China The Northwestern Polytechnical University Xi’an China The Xiangya School of Pharmaceutical Sciences Central South University China AIDD Department of Yuyao Biotech Shanghai China Hunan University China The College of Information Science and Engineering Hunan University Changsha China Computer Science The University of Illinois Chicago United States

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time Multi-person Eyeblink Detection in the Wild for Un...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenzheng Zeng Yang Xiao Sicheng Wei Jinfang Gan Xintao Zhang Zhiguo Cao Zhiwen Fang Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from uncon-strainedfilms to reveal “in the wild“ characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counter-parts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

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Mobile Crowd-Sensing System Based on Participant Selection 15th

Mobile Crowd-Sensing System Based on Participant Selection

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Tu, Chunyu Han, Lei Wang, Leye Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China Key Lab of High Confidence Software Technologies Peking University Beijing China

ISBN: (纸本)9783030642426

Mobile Crowdsensing (MCS) is often accompanied by various adverse objectives when performing data collection, which makes it difficult to collect accurate data of the entire target area with low cost. Therefore, how to collect a small part of the data to accurately infer the other data of the entire target area is a crucial issue. People’s daily trajectories usually follow a certain pattern, for example, students go to school, company employees go to work. This pattern allows us to find participants who often pass through fixed areas within a certain time span, leading their collected partial data are near optimal for the entire data collection task. This paper models the problem and develops multiple methods to improve the participant selection and the data recovery. Particularly, we use a random method, reinforcement learning, and greedy algorithm to handle this problem and compare the differences among these methods by experiments. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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A Dynamic Multi-Objective Evolutionary Algorithm Based on Niche Prediction Strategy

SSRN

引用

SSRN 2022年

作者： Zheng, Jinhua Zhang, Bo Zou, Juan Yang, Shengxiang Hu, Yaru Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Xiangtan University Hunan Province Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front (PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) changes as the environment change. Therefore, solving such problems requires an optimization algorithm that can quickly track the PF or PS after an environment change. Prediction-based response mechanism is a common method used to deal with environmental changes, which is commonly known as center point-based prediction. However, if the predicted direction of the center point is inaccurate, the predicted population will be biased towards one side. In this paper, we propose a niche prediction strategy based on center and boundary points (PCPB) to solve the dynamic multi-objective optimization problems, which consists of three steps. After environmental changes are detected, the first step is to divide the niche, dividing different individuals in the PS into different niche populations. The second step is to independently predict different niches, and select individuals with good convergence and distribution in the niche to predict the individuals that will produce the next generation. Finally, some different individuals are randomly generated in the next possible PS area to ensure the diversity of the population. To verify whether our proposed strategy is effective and competitive, PCPB was compared with five state-of-the-art strategies. The experimental results show that PCPB performed competitively in solving dynamic multi-objective optimization problems, which proves that our algorithm has good competitiveness. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Multi-interactive dual-decoder for RGB-thermal salient object detection

arXiv

引用

arXiv 2020年

作者： Tu, Zhengzheng Li, Zhun Li, Chenglong Lang, Yang Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGB-thermal salient object detection (SOD) aims to segment the common prominent regions of visible image and corresponding thermal infrared image that we call it RGBT SOD. Existing methods don’t fully explore and exploit the potentials of complementarity of different modalities and multi-type cues of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive dual-decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode two modalities into multi-level multi-modal feature representations. Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Finally, we carry out extensive experiments on public RGBT and RGBD SOD datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has been released at: https://***/lz118/Multi-interactiveDual-decoder. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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An axial-ratio beam-width enhancement of antenna based on electromagnetic bandgap (EBG)

An axial-ratio beam-width enhancement of antenna based on el...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Shijie Huang Liang Zhang Chao Wang Changqing Liu Shenbing Wu Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

In order to meet the requirements of the navigation system for antennas with wide 3-dB AR beam-width, this paper proposes an axial-ratio beam-width enhancement of antenna based on electromagnetic bandgap (EBG). The effect of surface wave on the circular polarization of the antenna is suppressed by loading two layers of mushroom-like EBG unit cells around the patch. The loading of the EBG increases the 3-dB AR beam-width of the antenna by 78% in the xoz plane and the by 84% in the yoz plane, the simulation and test results demonstrate the effectiveness of this method.

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