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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Li, Yanan Yu, Jun Zhan, Yibing Chen, Zhi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450383080

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN. © 2021 ACM.

关键词： Semantics

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Improved Mayfly Algorithm Based on Co-Evolution for Feature Selection

Improved Mayfly Algorithm Based on Co-Evolution for Feature ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Guo Tianyu Dong Hongbin Zhou Jing National Engineering Laboratory for E-government Modeling and Simulation School of Computer Science and Technology Harbin Engineering University Harbin China

Mayfly algorithm (MA) is a new type of intelligent optimization algorithm with excellent search ability and a broad application prospect in feature selection. However, as the dimension of the data increases, MA also has the problem of weak search ability and falling into the local optimal solution. To solve these problems, this paper combines the idea of collaborative evolution and proposes an improved Mayfly algorithm (CEIMA) suitable for feature selection problems. The CEIMA algorithm first divides the population into two sub-populations and initializes them in different ways to increase the diversity of the population. During evolution, information sharing is achieved through an information transmission mechanism between sub-populations, achieving collaborative evolution between sub-populations to enhance the global search ability of CEIMA. Finally, the mRMR-m strategy is proposed to mutate the global optimal individual based on feature importance, improving the ability of CEIMA to escape from local optimal solutions. Through a comparison with 7 feature selection algorithms on 12 UCI datasets, the effectiveness of CEIMA is proven.

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering

Prompting Large Language Models with Answer Heuristics for K...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhenwei Shao Zhou Yu Meng Wang Jun Yu Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have sought to use a large language model (i.e., GPT-3 [3]) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of GPT-3 as the provided input information is insufficient. In this paper, we present Prophet-a conceptually simple framework designed to $prompt$ GPT-3 with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their testing sets, respectively.

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Organic–inorganic hybrid perovskite scintillators for mixed field radiation detection

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InfoMat 2022年第9期4卷 97-108页

作者： Mengling Xia Guangda Niu Linyue Liu Runlong Gao Tong Jin Pengying Wan Weicheng Pan Xianpeng Zhang Zuoxiang Xie Sam Teale Zenghua Cai Jiajun Luo Shan Zhao Haodi Wu Shiyou Chen Zhiping Zheng Qingguo Xie Xiaoping Ouyang Edward HSargent Jiang Tang Wuhan National Laboratory for Optoelectronics(WNLO) WuhanChina School of Optical and Electronic Information Huazhong University of Science and Technology(HUST)WuhanChina State Key Laboratory of Intense Pulsed Radiation Simulation and Effect Northwest Institute of Nuclear TechnologyXi'anChina School of Nuclear Science and Technology Xi'an Jiaotong UniversityXi'anChina Department of Electrical and Computer Engineering University of TorontoTorontoOntarioCanada School of Microelectronics Fudan UniversityShanghaiChina College of Life Science and Technology Huazhong University of Science and TechnologyWuhanChina

Sensitive and fast detection of neutrons and gamma rays is vital for homeland security,high-energy physics,and proton ***-neutron detectors rely on light organic scintillators,andγ-ray detectors use heavy inorganic scintillators and *** mixed-field detection using a single material is highly challenging due to their contradictory *** we report hybrid perovskites(C_(8)H_(12)N)_(2)Pb(Br_(0.95)Cl_(0.05))_(4)that combine light organic cations and heavy inorganic skeletons at a molecular level to achieve unprecedented performance for mixed-field radiation *** neutron absorption due to a high density of hydrogen,strong radiative recombination within the highly confined[PbX_(6)]^(4-)layer,and sub-nanometer distance between absorption sites and radiative centers,enable a light yield of 41000 photons/MeV,detection pulse width of 2.97 ns and extraordinary linearity response toward both fast neutrons andγ-rays,outperforming commonly used fast-neutron *** energy spectrum,time-of-flight based fast-neutron/γ-ray discrimination and neutron yield monitoring were all successfully achieved using(C_(8)H_(12)N)_(2)Pb(Br_(0.95)Cl_(0.05))_(4)*** further demonstrate the monitoring of reaction kinetics and total power of a nuclear fusion *** envision that molecular hybridized scintillators open a new avenue for mixed-field radiation detection and imaging.

关键词： fast neutron mixed-field radiation detection organic-inorganic hybrid perovskite

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph Naïve Bayes syndrome differentiation

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of Complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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2021 International Conference on Applied Mathematics, Modeling and computer simulation, AMMCS 2021

作者： Zhu, Huawei Mao, Yihao Zhao, Yiming Shi, Liyuan Shao, Yanli School of Information and Electrical Engineering Zhejiang University City College Hangzhou310015 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781643682549

With the development of urbanization, the number of residents' motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation ***, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction. © 2022 The authors and IOS Press.

关键词： Traffic congestion

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Optimization of Breast Cancer Classification with Octave Bands Analysis

Optimization of Breast Cancer Classification with Octave Ban...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Iulia-Nela Anghelache Nastase Simona Moldovanu Luminita Moraru Emil Racovita Theoretical Highschool The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania Department of Computer Science and Information Technology The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania Department of Chemistry Physics & Environment The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania Department of Physics School of Science and Technology Sefako Makgatho Health Sciences University Pretoria South Africa

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Recent advancements in medical imaging technologies with a particular focus on the integration of artificial intelligence in image analysis, show potential in tackling clinical challenges related to the detection of breast cancer, evaluating treatment responses, and monitoring the progression of the disease. In this paper, a new algorithm based on bands that cross the lesion (three verticals, and three horizontals), with widths of 2, 4, and 8 pixels to classify breast lesions is proposed. A new octave band analysis is proposed to optimize the model’s feature extraction. Thus, each selected band is split into twelve sub-bands, and seventy-two features are obtained. The vector features dimensionality is reduced based on the features’ meaningfulness. To overcome the various machine learning models that fail to accurately classify these images due to their complex and diverse nature, several Automated Machine Learning libraries (AutoML) with PyCaret are used. They tune the hyperparameters for each selected classifier. For each selected band only eight features feed an AutoML PyCaret algorithm. In the case of the band having a width of 4 pixels, the binary classification results provide an accuracy of 0.812, an Area Under the Curve (AUC) of 0.844, and an F1-score of 0.867. The results are validated by the bagging method. It provided for the test dataset an accuracy of 0.813.

关键词： Analytical models Accuracy Ultrasonic imaging Shape Bandwidth Feature extraction Breast cancer Vectors Classification algorithms Lesions

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Stochastic model of noise for a quantum thermal transistor

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Physical Review B 2023年第23期108卷 235421-235421页

作者： Uthpala N. Ekanayake Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

Our focus in this investigation lies in developing a noise model for a quantum thermal transistor model inspired by its electronic counterpart, with the primary aim of establishing a platform for constructing analogous models. Previous studies on coupled two-level systems-based thermal transistors were focused on their average energy exchange. In this paper, we shift our attention to exploring the stochastic behavior of such thermal transistors due to the disturbances caused to their environment, such as continuous measurements. In the literature, the master equation for the transistor model is derived using the reduced dynamics method. This way, it masks the study of the stochastic nature of the energy flows in the system due to disturbances to the environment. In this paper, we describe a quantum trajectory under measurement theory whose ensemble average unravels the master equation for a quantum thermal transistor. This allows us to analyze the fluctuations and noise levels in the transistor model with greater detail. Then, we produce a numerical solution for the transistor dynamics based on Euler-Maruyama approximation. This helps to establish a model for the thermal transistor, drawing parallels to the small-signal/noise model in an electronic transistor. We define two parameters, thermal conductance and output thermal resistance, to describe the small signal-like model for the thermal transistor. Through these investigations, we seek to gain insights that can help design advanced heat management devices at the quantum level.

关键词： Open quantum systems Quantum gates Thermal properties Nanotechnology Transistors

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