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Path test data generation using adaptive simulated annealing particle swarm optimization

Soft computing 2024年第17-18期28卷 9587-9607页

作者： Jiao, Chongyang Zhou, Qinglei State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China Henan Information Engineering School Zhengzhou Vocational College of Industrial Safety Zhengzhou450000 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

Software testing is an effective means of ensuring software quality. The cost of software testing is the main component of the total cost of software development. The generation of test data is very important in testing, because the efficiency of testing depends on the test data used. A significant part of software testing automation is the test data generation. How to automatically generate test datasets is still an open problem. Search-based software testing (SBST) is a process of generating test data that employs meta-heuristic methods to resolve tough NP-hard problems. The test data generation problem belongs to NP-hard problems. This paper focuses on the automatic generation of test data for path coverage based on control flow criteria in structural testing. Some meta-heuristic algorithms have been used to search for software test data. To advance the efficiency and effectiveness of path test data generation, an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed. The probabilistic jumping property of simulated annealing (SA) algorithm is introduced into the particle swarm optimization (PSO) algorithm to make the particle accept bad solution at a certain probability during the searching process, and therefore boost the capacity of the proposed algorithm to bounce from the local optimum. The fitness function is constructed by the branch function superposition to properly guide the search process. In addition, to improve the convergence speed of the algorithm, an adaptive adjustment scheme based on inertial weight and an adjustment scheme based on learning factor are proposed as well. The experimental results show that the proposed method can effectually avoid premature convergence and effectively upgrades the efficiency of generating test data automatically. It is also competitive in solving other complicated optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Natur

关键词： Software testing

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Organic-inorganic hybrid hole transport layers with SnS doping boost the performance of perovskite solar cells

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Journal of Energy Chemistry 2022年第5期31卷 637-645页

作者： Xiaolu Zheng Haibing Wang Feihong Ye Cong Chen Weijun Ke Wenjing Zhang Chuanxin He Yanlong Tai Guojia Fang College of Chemistry and Environmental Engineering Institute of Microscale OptoelectronicsShenzhen UniversityShenzhen 518060GuangdongChina Shenzhen Institute Wuhan UniversityShenzhen 518057GuangdongChina Key Laboratory of Human-Machine Intelligence-Synergy Systems of Chinese Academy of Sciences and Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina

Perovskite solar cells(PSCs) have demonstrated excellent photovoltaic performance which currently rival the long-standing silicon solar cells’ efficiency. However, the relatively poor device operational stability of PSCs still limits their future commercialization. Binary sulfide is a category of materials with promising optoelectrical properties, which shows the potential to improve both the efficiency and stability of *** we demonstrate that the inorganic tin monosulfide(Sn S) can be an efficient dopant in 2,2’,7,7’-tet rakis(N,N-di-p-methoxy-phenylamine)-9,9’-spirobifluorene(spiro-OMe TAD) to form a composite hole transport layer(HTL) for PSCs. Sn S nanoparticles(NPs) synthesized through a simple chemical precipitation method exhibit good crystallization and suitable band matching with the perovskites. The introduction of Sn S NPs in Spiro-OMTAD HTLs enhanced charge extraction, reduced trap state density, and shallowed trap state energy level of the devices based on the composite HTLs. Therefore, the resulting solar cells employing Sn S-doped spiro-OMe TAD HTLs delivered an improved stabilized power output efficiency of 21.75% as well as enhanced long-term stability and operational stability. Our results provide a simple method to modify the conventional spiro-OMe TAD and obtain PSCs with both high efficiency and good stability.

关键词： Perovskite solar cells SnS nanoparticles Trap state passivation Stability

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MaintaAvatar: A Maintainable Avatar Based on Neural Radiance Fields by Continual Learning

arXiv

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arXiv 2025年

作者： Gu, Shengbo Qiu, Yu-Kun Tang, Yu-Ming Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The generation of a virtual digital avatar is a crucial research topic in the field of computer vision. Many existing works utilize Neural Radiance Fields (NeRF) to address this issue and have achieved impressive results. However, previous works assume the images of the training person are available and fixed while the appearances and poses of a subject could constantly change and increase in real-world scenarios. How to update the human avatar but also maintain the ability to render the old appearance of the person is a practical challenge. One trivial solution is to combine the existing virtual avatar models based on NeRF with continual learning methods. However, there are some critical issues in this approach: learning new appearances and poses can cause the model to forget past information, which in turn leads to a degradation in the rendering quality of past appearances, especially color bleeding issues, and incorrect human body poses. In this work, we propose a maintainable avatar (MaintaAvatar) based on neural radiance fields by continual learning, which resolves the issues by utilizing a Global-Local Joint Storage Module and a Pose Distillation Module. Overall, our model requires only limited data collection to quickly fine-tune the model while avoiding catastrophic forgetting, thus achieving a maintainable virtual avatar. The experimental results validate the effectiveness of our MaintaAvatar model. © 2025, CC BY.

关键词： Federated learning

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Research on Sleep Staging Method Based on Multi-Scale Convolution and Self-Attention Mechanism 2

Research on Sleep Staging Method Based on Multi-Scale Convol...

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2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Pan, Dongdong Li, Yingying School of Computer and Artificial Intelligence Zhengzhou University China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University China

ISBN: (纸本)9781510666733

Sleep is a very important physiological process for humans, and sleep staging is an important basis for evaluating sleep quality and diagnosing sleep-related diseases. Manual sleep staging is time-consuming and laborious, and the staging results are easily affected by subjective factors. Therefore, the research on automatic sleep staging models has extremely high research value and clinical application value. This paper proposes a sleep staging method based on multi-scale one-dimensional convolutional neural network and self-attention mechanism. The attention mechanism mines the channel correlation and temporal correlation of different channels respectively. This paper selects the sleep data of 153 normal subjects in the public dataset Sleep-EDF for model training and testing. The average staging accuracy rate was 84.5%, and the consistency test value was 0.781, which was better than the consistency test value of 0.75 for the inter-physician score. The experimental results show that the algorithm model in this paper has a high staging effect, can make full use of sleep data, and has important research significance for assisting clinicians in rapid sleep staging. © 2023 SPIE.

关键词： Convolution

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iManip: Skill-Incremental Learning for Robotic Manipulation

arXiv

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arXiv 2025年

作者： Zheng, Zexin Cai, Jia-Feng Wu, Xiao-Ming Wei, Yi-Lin Tang, Yu-Ming Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Pengcheng Lab China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task, skill-incremental learning, in robotic manipulation, which is to endow the robots with the ability to learn new manipulation skills based on the previous learned knowledge without re-training. First, we build a skill-incremental environment based on the RLBench benchmark, and explore how traditional incremental methods perform in this setting. We find that they suffer from severe catastrophic forgetting due to the previous methods on classification overlooking the characteristics of temporality and action complexity in robotic manipulation tasks. Towards this end, we propose an incremental Manipulation framework, termed iManip, to mitigate the above issues. We firstly design a temporal replay strategy to maintain the integrity of old skills when learning new skill. Moreover, we propose the extendable PerceiverIO, consisting of an action prompt with extendable weight to adapt to new action primitives in new skill. Extensive experiments show that our framework performs well in Skill-Incremental Learning. Codes of the skill-incremental environment with our framework will be open-source. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot learning

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AQMFL: An Adaptive Quantization Framework for Multi-modal Federated Learning in Heterogeneous Edge Devices

AQMFL: An Adaptive Quantization Framework for Multi-modal Fe...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Haoyong Tang Kaixuan Zhang Jiuwu Zhang Xin Xie Xinyu Tong Xiulong Liu College of Intelligence and Computing Tianjin University China Tianjin Key Laboratory of Advanced Networking (TANK)

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the wide application of multi-modal fusion sensing in scenarios such as autonomous driving and human-computer interaction, the privacy security and communication burden caused by massive data uploading need to be solved urgently. Federated Learning (FL) has received significant attention as a privacy-preserving distributed machine learning paradigm. Recent Multi-Modal Federated Learning (MMFL) focuses on addressing modal heterogeneity to enhance accuracy and speed up convergence. However, it overlooks the huge communication overhead in updating complex multi-modal network models, especially in edge environments with limited bandwidth. At the same time, the state-of-the-art communication-efficient FL methods are not customized to the MMFL characteristics. In this paper, we propose the Adaptive Quantization framework for Multi-modal Federated Learning (AQMFL). AQMFL implements decision-level multi-modal fusion locally by using parallel training and model ensemble, supporting its adaptation to modal heterogeneity and flexible deployment. AQMFL can adaptively allocate the number of quantization levels of gradient according to the modal contribution and the heterogeneous communication ability of nodes, which speeds up the system convergence and achieves a better balance between accuracy and communication efficiency. Compared with the classical baselines, AQMFL can reduce the total communication overhead by up to 50.47% and the total training time by up to 52.11% while maintaining the accuracy.

关键词： Training Human computer interaction Adaptation models Distributed processing Quantization (signal) Accuracy Federated learning Sensors Security Convergence

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NECA: Neural Customizable Human Avatar

NECA: Neural Customizable Human Avatar

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Junjin Xiao Qing Zhang Zhan Xu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Adobe Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human avatar has become a novel type of 3D asset with various applications. Ideally, a human avatar should be fully customizable to accommodate different settings and environments. In this work, we introduce NECA, an approach capable of learning versatile human representation from monocular or sparse-view videos, enabling granular customization across aspects such as pose, shadow, shape, lighting and texture. The core of our approach is to represent humans in complementary dual spaces and predict disentangled neural fields of geometry, albedo, shadow, as well as an external lighting, from which we are able to derive realistic rendering with high-frequency details via volumetric rendering. Extensive experiments demonstrate the advantage of our method over the state-of-the-art methods in photorealistic rendering, as well as various editing tasks such as novel pose synthesis and relighting. Our code is available at https://***/iSEE-laboratory/NECA.

关键词： Geometry Computer vision Three-dimensional displays Codes Shape Avatars Lighting

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A Review of State-of-the-arts Sensing Techniques for the Ultra-early Screening of Cerebral Palsy

A Review of State-of-the-arts Sensing Techniques for the Ult...

引用

2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Zhuang, Yunji Zhou, Hao Zhang, Huiping Chen, Yanni Wang, Lin Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Xi'an Jiaotong University Children's Hospital Xi'an710002 China

ISBN: (纸本)9798350327182

Cerebral palsy (CP) is characterized by neuromotor disorders. It remains a huge challenge for ultra-early screening of CP. So far, the diagnosis of CP is mainly based on a combination of neuromotor assessment methods, magnetic resonance imaging (MRI) and clinical history. However, subjective errors are highly likely to be introduced in the process of CP screening due to disparities in clinicians' professional backgrounds, working abilities and academic levels. Cutting-edge sensing techniques have been unveiled in the field of ultra-early CP prediction which provides objective diagnosis with quantified output information by obtaining motion capture, classification, and recognition. It is necessary to contrast the strengths, weaknesses, and potential of these techniques and how to apply them in the ultra-early screening of CP. This study compares and analyzes the latest and advanced sensing methods and discusses their future applications in infantile CP screening. The work could provide technical suggestions and clinical guidance for early intervention in the field of ultra-early CP screening later on. © 2023 IEEE.

关键词： Diseases

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An Instance-Level Motion-Aware Graph Model for Multi-target Multi-camera Tracking

An Instance-Level Motion-Aware Graph Model for Multi-target ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiaotong Fan Huicheng Zheng School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multi-target multi-camera tracking (MTMCT), focusing on inferring trajectory across multiple surveillance videos, is holding significant practical utility. While numerous studies aim to learn visual features robust to illumination variation, occlusions, and other issues, the spatio-temporal information within a multi-camera system is not explored sufficiently. Existing methods generally exploit coarse-grained spatio-temporal information by modeling the distribution of time intervals between cameras. However, they tend to neglect the specific motion state of individual objects, which may hinder accurate cross-camera association. In this paper, we introduce a novel motionaware graph (MAG) model designed to extract instance-level spatio-temporal information that aligns with visual features and seamlessly aggregate these two kinds of information within a unified graph framework for MTMCT. Specifically, we propose a motion encoder-decoder module that predicts spatio-temporal consistency scores between objects based on their instance-level motion states. These scores are then integrated with visual similarity scores to generate discriminative feature representations for data association via a graph attention mechanism. Experimental evaluations and ablation studies on the large-scale MTA dataset demonstrate the superiority of our proposed model.

关键词： Visualization Attention mechanisms Tracking Surveillance Neural networks Lighting Predictive models

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DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation

arXiv

引用

arXiv 2024年

作者： Yan, Junkai Gao, Yipeng Yang, Qize Wei, Xihan Xie, Xuansong Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Institute for Intelligent Computing Alibaba Group China

Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed. However, a challenge arises when the specific appearances need customizing at designated viewpoints but referring solely to the overall description for generating 3D objects. For instance, ambiguity easily occurs when producing a T-shirt with distinct patterns on its front and back using a single overall text guidance. In this work, we propose DreamView, a text-to-image approach enabling multi-view customization while maintaining overall consistency by adaptively injecting the view-specific and overall text guidance through a collaborative text guidance injection module, which can also be lifted to 3D generation via score distillation sampling. DreamView is trained with large-scale rendered multi-view images and their corresponding view-specific texts to learn to balance the separate content manipulation in each view and the global consistency of the overall object, resulting in a dual achievement of customization and consistency. Consequently, DreamView empowers artists to design 3D objects creatively, fostering the creation of more innovative and diverse 3D assets. Code and model will be released at here. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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