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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Gao, Mingchen Wang, Huiyan Xu, Chang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350330663

Constraint checking techniques are being widely used for ensuring the consistency of software artifacts during their development and evolution (e.g., detecting inconsistency in an application's running contexts or identifying rule violation in the code being developed). Typically, consistency constraints are formulated and checked upon the changes of software artifacts under checking. When any constraint is violated, an inconsistency is said to occur and then follow-up actions can be taken to remedy the problem. Currently, various constraint checking techniques have been proposed and implemented with sophisticated mechanisms for higher efficiency and scalability. However, these implementations could be far from being well tested due to their oracle problems, i.e., hardly able to tell what the checking result should be, given any software artifacts and their consistency constraints to check. In this paper, we leverage metamorphic testing and propose a family of metamorphic relations catered for testing constraint checking implementations. We dedicatedly design these relations by following the sensitivity principle via a fine-granularity control and the diversity principle via input-oriented transformations. Our experiments reported promising results (disclosing 80 % mutation bugs and five real bugs) without the need of any manual labeling. © 2024 IEEE.

关键词： Software testing

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LAMB: Label-Induced Mixed-Level Blending for Multimodal Multi-label Emotion Detection 19th

LAMB: Label-Induced Mixed-Level Blending for Multimodal Mul...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Qian, Shuwei Guo, Ming Fan, Zhicheng Chen, Mingcai Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031545276

To better understand complex human emotions, there is growing interest in utilizing heterogeneous sensory data to detect multiple co-occurring emotions. However, existing studies have focused on extracting static information from each modality, while overlooking various interactions within and between modalities. Additionally, the label-to-modality and label-to-label dependencies still lack exploration. In this paper, we propose LAbel-induced Mixed-level Blending (LAMB) to address these challenges. Mixed-level blending leverages shallow but manifold self-attention and cross-attention encoders in parallel to model unimodal context dependency and cross-modal interaction simultaneously. This is in contrast to previous works either use one of them or cascade them successively, which ignores the diversity of interaction in multimodal data. LAMB also employs label-induced aggregation to allow different labels to attend to the most relevant blended tokens adaptively using a transformer-based decoder, which facilitates the exploration of label-to-modality dependency. Unlike common low-order strategies in multi-label learning, correlations among multiple labels can be learned by self-attention in label embedding space before being treated as queries. Comprehensive experiments demonstrate the effectiveness of our methods for multimodal multi-label emotion detection. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Emotion Recognition

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Sub-nanometer structured silicon-carbon composite nanolayers armoring on graphite for fast-charging and high-energy-density lithium-ion batteries

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Rare Metals 2023年第11期42卷 3692-3704页

作者： Zhen-Wei Li Mei-Sheng Han Jie Yu Songshan Lake Materials Laboratory Dongguan523808China Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Shenzhen Engineering Lab for Supercapacitor MaterialsSchool of Material Science and EngineeringHarbin Institute of TechnologyShenzhenShenzhen 518055China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen518055China

Silicon/carbon composites are promising alternatives to current graphite anodes in commercial lithiumion batteries(LIBs)because of their high capacity and excellent ***,the unsatisfactory fastcharging capability and cycle stability of Si/C composites caused by slow charge transport capability and huge volume change under industrial electrode conditions severely hamper their ***,a novel Si/C anode was fabricated by homogeneously depositing amorphous C-Si nanolayers on graphite(C-Si@graphite).C-Si nanolayers with uniformly dispersed sub-nanometer Si particles in 3D carbon skeleton significantly boost electron and Li-ion transport and efficiently relieve Si's agglomeration and volume *** a result,the tailored C-Si@graphite electrodes show an excellent rate capacity(760.3 mAh·g^(-1)at 5.0C)and long cycle life of over 1000 cycles at 1.0C and800 cycles at 2.0C under industrial electrode *** addition,the assembled full cells(C-Si@graphite,anode;Li[Ni_(0.8)Co_(0.1)Mn_(0.1)]O_(2),cathode)present superior fastcharging capability(240.4 Wh·kg^(-1),charging for16.2 min,3.0C)and long cycle life(80.7%capacity retention after 500 cycles at 1.0C),demonstrating the massive potential of C-Si@graphite for practical application.

关键词： Si/C anode Sub-nanometer Si Fast-charging capability Lithium-ion batteries(LIBs)

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Overcoming the Limits of Cross-Sensitivity:Pattern Recognition Methods for Chemiresistive Gas Sensor Array

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Nano-Micro Letters 2024年第12期16卷 285-341页

作者： Haixia Mei Jingyi Peng Tao Wang Tingting Zhou Hongran Zhao Tong Zhang Zhi Yang Key Lab Intelligent Rehabil&Barrier Free Disable(Ministry of Education) Changchun UniversityChangchun 130022People’s Republic of China Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237People’s Republic of China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Department of Micro/Nano ElectronicsSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012People’s Republic of China

As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing *** recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas *** is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction *** this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas *** further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing ***,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas *** the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical *** conclusion,this study anticipates future research prospects by considering the existing landscape and *** is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

关键词： Pattern recognition Sensor array Chemiresistive gas sensor Cross-sensitivity Artificial olfactory

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Detail Enhancement for Free Surface LBM Using Adaptive Sizing of Coupled Particles 1st

Detail Enhancement for Free Surface LBM Using Adaptive Sizi...

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1st International Conference on Extended Reality, ICXR 2024

作者： Qu, Qingyue Liu, Huiwen Zhu, Shaonan Hao, Aimin Yu, Peng Gao, Yang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science Yangtze Power Co. Ltd. Hubei Yichang443000 China

ISBN: (纸本)9789819636785

Physically based fluid simulation plays an important role in the immersion and realistic of VR and AR applications. However, efficiency is the bottleneck problem that restricts physical applications. Among those physics-related fluid models, the Lattice Boltzmann Method (LBM), with its localized algorithm, offers high efficiency. However, as a purely Eulerian grid-based approach, achieving rich detail necessitates high resolution. Conversely, Lagrange-based methods can capture intricate details but require substantial computational time to solve large-scale linear systems and manage numerous particle operations. To address these challenges, we propose a hybrid solver that couples the efficient LBM solver with an adaptive particle interpolation method for detail enhancement. Our innovative adaptive particle strategy further reduces particle count while preserving surface detail and improving temporal efficiency by dynamically adjusting particle size based on particle position, distance, and velocity. Experiments demonstrate that our method can display more surface details than LBM alone, delivering visual effects comparable to direct particle interpolation but with higher efficiency. Moreover, our method yields superior visual results compared to adaptive particle methods based on the FLIP approach. This approach is particularly well-suited for large-scale simulations, enhancing both visual quality and time efficiency, and shows great potential for real-time fluid simulations in VR applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computational fluid dynamics

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A quantum‐like approach for text generation from knowledge graphs

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1455-1463页

作者： Jia Zhu Xiaodong Ma Zhihao Lin Pasquale De Meo Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhuaChina School of Computer Science South China Normal UniversityGuangzhouChina DICAM Department University of MessinaMessinaItaly

Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge *** all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph *** local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding *** methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.

关键词： data mining knowledge‐based vision machine learning natural language processing text analysis

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Fractal Measurement of Images Human Brain from MRI

Fractal Measurement of Images Human Brain from MRI

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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CASSOR: Class-Aware Sample Selection for Ordinal Regression with Noisy Labels 20th

CASSOR: Class-Aware Sample Selection for Ordinal Regression...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Yuan, Yue Wan, Sheng Zhang, Chuang Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Security Nanjing China

ISBN: (纸本)9789819970216

Ordinal regression aims at solving the classification problem, where the categories are related in a natural order. Due to the difficulty in distinguishing between highly relevant categories, label noise is frequently present in ordinal data. Moreover, the varying degrees of relevance between categories can lead to an inconsistent distribution of misclassification loss across categories, posing a challenge to select clean data consistently from all categories for training. To overcome this limitation, we develop a sample selection method termed ‘Class-Aware Sample Selection for Ordinal Regression’ (CASSOR). To be concrete, we devise a class-specific sample selection strategy in order to adaptively acquire sufficient clean examples for robust model training. Moreover, a label-ranking regularizer is designed to help guide the sample selection process via exploring the ordinal relationship between different examples. As a result, our proposed CASSOR is endowed with strong discrimination abilities on ordinal data. Intensive experiments have been performed on multiple real-world ordinal regression datasets, which firmly demonstrates the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Regression analysis

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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