A number of devices in Industrial Internet are various types in recent years. The monitored traffic data from different devices always unlabeled and contain various types of attack traffic. In other words, misjudgment...
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This paper introduces a novel RISC-V processor architecture designed for ultra-low-power and energy-efficient applications,particularly for Internet of things(IoT)*** architecture enables runtime dynamic reconfigurati...
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This paper introduces a novel RISC-V processor architecture designed for ultra-low-power and energy-efficient applications,particularly for Internet of things(IoT)*** architecture enables runtime dynamic reconfiguration of the datapath,allowing efficient balancing between computational performance and power *** is achieved through interchangeable components and clock gating mechanisms,which help the processor adapt to varying workloads.A prototype of the architecture was implemented on a Xilinx Artix 7 field programmable gate array(FPGA).Experimental results show significant improvements in power efficiency and *** mini configuration achieves an impressive reduction in power consumption,using only 36%of the baseline ***,the full configuration boosts performance by 8%over the *** flexible and adaptable nature of this architecture makes it highly suitable for a wide range of low-power IoT applications,providing an effective solution to meet the growing demands for energy efficiency in modern IoT devices.
Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or c...
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The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0,blockchain,and immersive *** paper presents a thorough analysis of the metaverse,showcasing its evolution from a c...
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The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0,blockchain,and immersive *** paper presents a thorough analysis of the metaverse,showcasing its evolution from a conceptual phase rooted in science fiction to a dynamic and transformative digital environment impacting various sectors including gaming,education,healthcare,and *** paper introduces the metaverse,details its historical development,and introduces key technologies that enable its existence such as virtual and augmented reality,blockchain,and artificial *** this work explores diverse application scenarios,future trends,and critical challenges including data privacy,technological limitations,and integration issues that must be addressed for the metaverse to reach its full *** significance of this study lies in its comprehensive nature,providing insights not only for researchers and practitioners but also for policymakers aiming to navigate the complexities of the metaverse and leverage its capabilities for societal ***,the paper forecast the future where the metaverse plays an integral role in reshaping human interaction,commerce,and creativity,thus emphasizing the need for ongoing research and collaborative efforts to unlock its vast possibilities.
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of *** distribu...
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With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of *** distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent *** address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing ***,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource *** novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT *** experiments demonstrate that optimizing the execution efficiency of components can significantly improve system *** instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%***,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%***,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance *** study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.
This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a coll...
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This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro
Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource *** to resource competition between...
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Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource *** to resource competition between batch jobs and online services,co-location frequently impairs the performance of online *** study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for *** performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on ***-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch *** the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling *** with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on *** show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.
With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft ***,the imbalance of electricity consumption data is violent,which mak...
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With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft ***,the imbalance of electricity consumption data is violent,which makes the training of detection model *** this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal *** this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption *** calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown *** the meanwhile,through training the network by different balanced subsets of training set,the prototype is *** with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal *** results show that the proposed method outperforms other state-of-the-art methods.
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In thi...
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Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.
To support early-career scientists in the initial stages of their independent careers,sciencechina Materials is proud to present the 2025 Emerging Investigator *** initiative emphasizes the recognition of emerging re...
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To support early-career scientists in the initial stages of their independent careers,sciencechina Materials is proud to present the 2025 Emerging Investigator *** initiative emphasizes the recognition of emerging researchers who demonstrate the potential to shape the future of their fields.
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