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Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Formal management-specifying approach for model-based safety assessment

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Journal of systems Engineering and Electronics 2023年第6期34卷 1589-1601页

作者： XU Changyi DUAN Yiman ZHANG Chao School of Control Science and Engineering Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of EducationDalian University of TechnologyDalian 116024China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China

In the field of model-based system assessment,mathematical models are used to interpret the system ***,the industrial systems in this intelligent era will be more *** management operations will be dynamically set,and the system will be no longer static as it is initially ***,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the *** mainly exists three ***:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the *** by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be ***,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

关键词： model-based safety assessment(MBSA) management availability reliability maintainability continuous time Markov chain.

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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On the upper bounds of (1,0)-super solutions for the regular balanced random (k,2s)-SAT problem

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Frontiers of computer science 2024年第4期18卷 139-146页

作者： Yongping WANG Daoyun XU Jincheng ZHOU School of Information Guizhou University of Finance and EconomicsGuiyang 550025China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Computer and Information Qiannan Normal University for NationalitiesDuyun 558000China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun 558000China

This paper explores the conditions which make a regular balancedrandom(k,2s)-CNFformula(1,O)-unsatisfiable with high *** conditions also make a random instance of the regular balanced(k-1,2(k-1)s)-SAT problem unsatisfiable with high probability,where the instance obeys a distribution which differs from the distribution obeyed by a regular balanced random(k-1,2(k-1)s)-CNF *** F be a regular balanced random(k,2s)-CNF formula where k≥3,then there exists a number so such that F is(1,O)-unsatisfiable with high probability if s>so.A numerical solution of the number so when k e(5,6,...,14)is given to conduct simulated *** simulated experiments verify the theoretical ***,the experiments also suggest that F is(1,O)-satisfiable with high probability if s is less than a certain value.

关键词： regular balanced random(k,2s)-SAT problem (1,0)-super solution upper bound

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Fast-Charging Protocols Design of Lithium-Ion Battery: A Multiple-Objective Bayesian Optimization Perspective

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8327-8338页

作者： Zhu, Rong Peng, Weiwen Yang, Fangfang Xie, Min City University of Hong Kong Department of Systems Engineering Hong Kong Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China

Fast-charging lithium-ion batteries are crucial for accelerating the adoption of electric vehicles (EVs) by reducing charging time and improving operational efficiency. However, fast charging presents a significant challenge due to the knee point in the battery degradation trajectory, beyond which capacity decreases rapidly. Optimizing fast-charging protocols that consider knee point capacity and cycle life is essential but requires extensive and costly cycling aging tests to obtain the necessary degradation labels. To address this challenge, a multiobjective Bayesian optimization framework is proposed for fast-charging protocol optimization, jointly considering knee point capacity and cycle life. To reduce experimental costs, two deep learning-based early prediction models are developed to predict knee point capacity and cycle life using data from the first 60 cycles. The framework employs a noisy expected hypervolume improvement acquisition function to handle prediction uncertainties during multiobjective optimization. Validation on publicly available battery datasets demonstrates that the proposed framework achieves effective optimization of fast-charging protocols while reducing experimental costs by approximately 90%. © 2015 IEEE.

关键词： Charging stations

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Development of wet adhesion of honeybee arolium incorporated polygonal structure with three-phase composite interfaces

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Friction 2024年第2期12卷 215-230页

作者： Lulu LIANG Jieliang ZHAO Qun NIU Li YU Xiangbing WU Wenzhong WANG Shaoze YAN Zhenglei YU School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Division of Intelligent and Biomechanical Systems State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing 100084China Key of Bionic Engineering Ministry of EducationJilin UniversityChangchun 130022China

Inspired by the dynamic wet adhesive systems in nature,various artificial adhesive surfaces have been developed but still face different ***,the theoretical mechanics of wet adhesives has never been sufficiently ***,we develop a novel adhesive mechanism for governing wet adhesion and investigate the biological models of honeybee arolium for reproducing the natural wet adhesive ***-nano structures of honeybee arolium and arolium-prints were observed by Cryogenic scanning electron microscopy(Cryo-SEM),and the air pockets were found in the contact interface ***,the adhesive models with a three-phase composite interface(including air pockets,liquid secretion,and hexagonal frames of arolium),were formed to analyze the wet adhesion of honeybee *** results of theoretical calculations and experiments indicated an enhanced adhesive mechanism of the honeybee by liquid self-sucking effects and air-embolism *** these effects,normal and shear adhesion can be adjusted by controlling the proportion of liquid secretion and air pockets in the contact ***,the air-embolism effects contribute to the optimal coupling of smaller normal adhesion with greater shear adhesion,which is beneficial for the high stride frequency of *** works can provide a fresh perspective on the development of bio-inspired wet adhesive surfaces.

关键词： honeybee arolium air pockets wet adhesion a three-phase interface self-sucking effects air-embolism effects

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Sparse representation scheme with enhanced medium pixel intensity for face recognition

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CAAI Transactions on Intelligence technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of computer science & technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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Stochastic Unit Commitment with High-Penetration Offshore Wind Power Generation in Typhoon Scenarios

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Journal of Modern Power systems and Clean Energy 2024年第2期12卷 535-546页

作者： Yanqi Liu Dundun Liu Hongcai Zhang State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China University of Macao Zhuhai UM Science and Technology Research Institute ZhuhaiChina

To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon ***,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal *** published papers only consider predefined typhoon trajectories neglecting *** address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon *** adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon *** formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.

关键词： Unit commitment two-stage stochastic programming offshore wind farm typhoon

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