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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Jiahui Yue, Kun Duan, Liang Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9781665492324

Extraction of relations among entities from texts is critical for domain knowledge representation. In this paper, an association graph was constructed to represent the dependencies among entities and relations, upon which a relation extraction method was proposed to predict the relations in domain texts. Experimental results on various annotated domain datasets demonstrate that the recall of our proposed method outperforms the other relation extraction models. © 2022 IEEE.

关键词： Extraction

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Convolutional Neural Network Based Detection of Seasonal Features in Time Series Data 11

Convolutional Neural Network Based Detection of Seasonal Fea...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hu, Kuang Yue, Kun School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9781665492324

Seasonal features of various kinds of time-varying nodes in large-scale complex networks could facilitate the effective network optimization. It is necessary to find the nodes with seasonal features from the limited labeled training set. In this paper, we proposed a sequence convolutional neural network model to detect the seasonal features in time series data, as well as the method of time series upscaling and subsequence embedding as data preprocessing. Experimental results show the effectiveness of our proposed model. © 2022 IEEE.

关键词： Embeddings

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A Brief Overview of ChatGPT:The History,Status Quo and Potential Future Development

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1122-1136页

作者： Tianyu Wu Shizhu He Jingping Liu Siqi Sun Kang Liu Qing-Long Han Yang Tang the Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and TechnologyShanghai 200237China the Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China the Research Institute of Intelligent Complex Systems Fudan UniversityShanghai 200437China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

ChatG PT,an artificial intelligence generated content (AIGC) model developed by OpenAI,has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of *** paper briefly provides an overview on the history,status quo and potential future development of ChatGPT,helping to provide an entry point to think about ***,from the limited open-accessed resources,we conclude the core techniques of ChatGPT,mainly including large-scale language models,in-context learning,reinforcement learning from human feedback and the key technical steps for developing *** further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various *** it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields,mankind should still treat and use ChatG PT properly to avoid the potential threat,e.g.,academic integrity and safety ***,we discuss several open problems as the potential development of ChatGPT.

关键词： AIGC ChatGPT GPT-3 GPT-4 human feedback large language models

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Trustworthy deep learning for encrypted traffic classification

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Soft computing 2025年第2期29卷 645-662页

作者： Li, Zheng Liu, Yanbei Zhang, Changqing Shan, Wanjin Zhang, Haifeng Zhu, Xiaoming School of Electronic and Information Engineering Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems Tianjin300387 China College of Intelligence and Computing Tianjin University Tianjin300072 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

Network traffic classification refers to the identification of collected network traffic data of various applications, which is widely used in research fields such as network resource allocation, traffic scheduling and intrusion detection systems. With the widespread application of encryption technology in the network, encrypted traffic classification has become a hot research topic. At present, most existing methods only focus on the accuracy of network traffic classification. Yet, few work studies the reliability of the classification model, which plays an important role in network regulation and network security. In this paper, we propose a novel traffic classification method based on trustworthy deep learning model, which can effectively improve the reliability of encrypted traffic classification models by correcting the confidence of model output. Specifically, we firstly perform data preprocessing on the original network traffic, and then adopt a ConvNet for feature learning and a ClassifyNet for traffic classification in the initial stage. At the same time, we utilize a trustworthy confidence criterion to design a ConfidNet trained according to the probability of the true class. The ConfidNet can provide a reliable confidence measure for the prediction of the classification model. Finally, we demonstrate the effectiveness of our framework through comprehensive experiments on two benchmark datasets ISCX VPN-nonVPN and USTC-TFC2016, and show that our method can improve the reliability of the classification model and has a good ability to identify misclassified samples compared with state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Network intrusion

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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Computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Confidence-Driven Semi-Supervised Partial Label Learning

Confidence-Driven Semi-Supervised Partial Label Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Chengkun Liu Jun Zhang Jing Chai School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming City China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Semi-supervised Partial Label Learning (SPLL) aims to learn from a dataset comprised of both partial label examples each of which is associated with a candidate label set and unlabeled examples. The mainstream of SPLL methods usually construct a confidence matrix for training examples and by which to operate label disambiguation and classifier training. However, they treat examples with different confidence levels in the same strategy during training, which might result in degenerated learning performance. In this work, a novel method named COnfidence-DRiven (CODR) is proposed to deal with the above drawback. In specific, we iteratively update the confidence matrix and predictive network, and employ different strategies to deal with high-confidence and remaining low-confidence examples. Extensive experiments on real-world datasets demonstrate the superiority of CODR in classification accuracy compared with several other state-of-the-art methods.

关键词： Training Measurement Accuracy Sensitivity analysis Noise Neural networks

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a cloud Data Center

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Journal of Computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Tailoring d-band center over electron traversing effect of Ni M@C-CoP(M=Zn,Mo,Ni,Co)for high-performance electrocatalysis hydrogen evolution

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Journal of Materials Science & Technology 2023年第35期166卷 164-172页

作者： Hao Luo Xudong Zhang Hui Zhu Kexin Zhang Fan Yang Kewei Xu Sen Yu Dagang Guo State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and EngineeringXi’an Jiaotong UniversityXi’an710049China Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an710049China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an710049China Shaanxi Key Laboratory of Biomedical Metal Materials Northwest Institute for Non-ferrous Metal ResearchXi’an710016China

Attaining a highly efficient and inexpensive electrocatalyst is significant for the hydrogen evolution reaction(HER)but still challenging *** transition-metal phosphides(TMPs)catalysts with platinum-like electronic structures are a potential candidate for the HER,but those are prone to be strongly bound with hydrogen intermediates(H∗),resulting in sluggish HER *** we report a unique hybrid structure of CoP anchored on graphene nanoscrolls@carbon nano tubes(CNTs)scaffold(Ni M@C-CoP)encapsulating various Ni M(M=Zn,Mo,Ni,Co)bimetal nanoalloy via chemical vapor deposi-tion(CVD)growth of CNT on graphene nanoscrolls followed by the impregnation of cobalt precursors and phosphorization for efficiently electrocatalytic hydrogen *** nanoparticles mainly scattered at the tip of CNT branches which exhibited the analogical“Three-layer core-shell”*** and density functional theory(DFT)calculations consistently disclose that the encapsulated various NiMs can offer different numbers of electrons to weaken the interactions of outmost CoP with H∗and push the downshift of the d-band center to different degrees as well as stabilize the outmost CoP nanopar-ticles to gain catalytic stability via the electron traversing *** electrocatalytic HER activity can be maximumly enhanced with low overpotentials of 78 mV(alkaline)and 89 mV(acidic)at a current density of 10 mA/cm^(2) and sustained at least 24 h especially for NiZn@C-CoP *** novel system is distinct from conventional three-layer heterostructure,providing a specially thought of d-band center control engineering strategy for the design of heterogeneous catalysts and expanding to other electrocat-alysts,energy storage,sensing,and other applications.

关键词： Electron traversing effect 3D graphene/CNT CoP nanoparticle d-band center Hydrogen evolution

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Parameter-efficient fine-tuning of Whisper for low-resource speech recognition

Parameter-efficient fine-tuning of Whisper for low-resource ...

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Artificial Intelligence, Networking and information Technology (AINIT), International Seminar on

作者： Yunpeng Liu Dan Qu Department of Artificial Intelligence Laboratory for Advanced Computing and Intelligence Engineering School of Information Systems Engineering University of Information Engineering Henan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available.

关键词： Seminars Costs Target recognition Error analysis Speech recognition Data models Task analysis

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