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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1711-1728页

作者： Jing-Doo Wang Chayadi Oktomy Noto Susanto departement of Computer Science and information Engineering Asia UniversityTaichungTaiwanChina departement of information Technology Universitas Muhammadiyah YogyakartaYogyakartaIndonesia

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic *** advancements in deep neural networks have enabled the development of models to represent traffic flow ***,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal *** paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention *** studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast *** addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast *** experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

关键词： Traffic flow prediction sptiotemporal data heterogeneous data Conv-BiLSTM data-centric intra-data

Embracing connected intelligence with the YuanOS architecture:one OS kit for all

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Frontiers of Computer Science 2024年第1期18卷 1-3页

作者： Haibo CHEN Ning JIA Jie YIN Institute of Parallel and Distributed systems School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China OS Kernel Lab Huawei Technologies Co.Ltd.Beijing 100085China

Operating system abstracts hardware and creates application execution environment,and thus is the key pillar for IT *** its first invention in 1956,operating system has been evolved over three main stages:Mainframe,PC&Internet,Mobile Internet[1].Now,the entire society is firmly stepping towards the connected intelligence era[2],how should operating system be evolved to embrace this new era?

关键词： hardware execution connected

Personality Trait Detection via Transfer Learning

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Computers, Materials & Continua 2024年第2期78卷 1933-1956页

作者： Bashar Alshouha Jesus Serrano-Guerrero Francisco Chiclana Francisco P.Romero Jose A.Olivas Department of information technologies and systems University of Castilla-La ManchaCiudad Real13071Spain Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a

关键词： Personality trait detection pre-trained language model big five model transfer learning

On optimal charging scheduling for electric vehicles with wind power generation

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Fundamental Research 2024年第4期4卷 951-960页

作者： Junjie Wu Qing-Shan Jia Center for Intelligent and Networked systems(CFINS) Department of AutomationBeijing National Research Center for Information Science and Technology(BNRist)TsinghuaUniversityBeijing100084China Huawei technologies Co.Ltd Shenzhen 518129China

We consider the scheduling of battery charging of electric vehicles(EVs)integrated with renewable power *** increasing adoption of EVs and the development of renewable energies contribute importance to this *** optimization of charging scheduling is challenging because of the large action space,the multi-stage decision making,and the high *** solve this problem is time-consuming when the scale of the system is *** is urgent to develop a practical and efficient method to properly schedule the charging of *** contribution of this work is ***,we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed *** algorithm is proposed to obtain the optimal charging policy when the sufficient condition ***,the scenario when the supply of the renewable power generation is deficient is *** prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first(mLLLP)***,we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general *** test the proposed algorithm by numerical *** results show that it performs better than the other existing rule-based methods.

关键词： Electric vehicle Charging scheduling Wind power Optimal policy Renewable energy

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni information systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

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Computers, Materials & Continua 2024年第6期79卷 4319-4347页

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection systems(NIDS)that can identify *** NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDS examines both risky and typical behavior using a variety of *** the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDS) NSL-KDD dataset deep-learning machine-learning CNN channel Attention network security

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of information systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

Geometric approach to robust quantum control for time-varying noises

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Science China(Physics,Mechanics & Astronomy) 2024年第9期67卷 197-198页

作者： Rebing Wu Center for Intelligent and Networked systems Department of AutomationTsinghua UniversityBeijing 100084China

Quantum technologies are expected to revolutionze modern information processing *** all quntum technologies demand high-precision control of quantum ***,maintaining the high-precision against noises is rather difficult[1].This requirement calls for robust control pulse design that is insensitive to *** practical implementation,the control pulses need to be smoothly shaped and be able to tackle parametric uncertainties or time-varying noises with complicated frequency characteristics.

关键词： quantum smoothly shaped

Broadband and fabrication-tolerant 3-dB couplers with topological valley edge modes

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Light(Science & Applications) 2024年第8期13卷 1623-1630页

作者： Guo-Jing Tang Xiao-Dong Chen Lu Sun Chao-Heng Guo Meng-Yu Li Zhong-Tao Tian Hou-Hong Chen Hong-Wei Wang Qi-Yao Sun Ying-Di Pan Xin-Tao He Yi-Kai Su Jian-Wen Dong School of Physics&State Key Laboratory of Optoelectronic Materials and technologies Sun Yat-sen UniversityGuangzhou 510275China State Key Laboratory of Advanced Optical Communication systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China

3-dB couplers,which are commonly used in photonic integrated circuits for on-chip information processing,precision measurement,and quantum computing,face challenges in achieving robust performance due to their limited 3-dB bandwidths and sensitivity to fabrication *** address this,we introduce topological physics to nanophotonics,developing a framework for topological 3-dB *** couplers exhibit broad working wavelength range and robustness against fabrication dimensional *** leveraging valley-Hall topology and mirror symmetry,the photonic-crystal-slab couplers achieve ideal 3-dB splitting characterized by a wavelength-insensitive scattering *** analysis confirms the superiority on broad bandwidth of 48 nm and robust splitting against dimensional errors of 20 *** further propose a topological interferometer for on-chip distance measurement,which also exhibits robustness against dimensional *** extension of topological principles to the fields of interferometers,may open up new possibilities for constructing robust wavelength division multiplexing,temperature-drift-insensitive sensing,and optical coherence tomography applications.

关键词： topological splitting insensitive

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

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Geography and Sustainability 2024年第2期5卷 211-219页

作者： Li Lin Liping Di Chen Zhang Liying Guo Haoteng Zhao Didarul Islam Hui Li Ziao Liu Gavin Middleton Center for Spatial information Science and systems George Mason UniversityFairfaxVA22030USA Department of Geography and Geoinformation Science George Mason UniversityFairfaxVA22030USA

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban *** mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely *** advancements in Geographic information systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based ***,challenges persist,particularly concerning accuracy and significant temporal *** study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing *** methodology can facilitate the accurate and timely identification of urban redevelopment *** study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban *** model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban *** research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban *** model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

关键词： Urban redevelopment Urban sustainability Remote sensing Time-series analysis Machine learning