The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions ...
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作者：
Babak FalsafiParallel Systems Architecture Laboratory
Institute of Computer and Communication SciencesSchool of Computer andCommunication SciencesEcole Polytechnique Fédérale de LausanneLausanneCH-1015Switzerland
Agile hardware design is an approach to developing hardware systems that draws inspiration from the principles and practices of agile software *** emphasizes collaboration,flexibility,iterative development,and quick a...
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Agile hardware design is an approach to developing hardware systems that draws inspiration from the principles and practices of agile software *** emphasizes collaboration,flexibility,iterative development,and quick adaptation to changing *** agile hardware design,the focus is on delivering functionalhardware systems in shorter development cycles while maintaining high-quality and customer *** particular,agile hardware design is of great interest in the open-source hardware ***-sourcehardware development—such as RISC-V—is at the forefront of initiatives to democratize hardware and drive innovation in chip design *** design is instrumental for the RISC-V community because it supportsrapid iteration,accommodates the evolving RISC-V standard and the addition of custom extensions,improvescommunity collaboration and time-to-market,and addresses the design challenges associated with complex architectural features.
At present, there is a growing demand for parallel mechanisms with fewer inputs and more outputs. These mechanisms are highly sought after in industries such as aerospace, antenna, and entertainment facilities, among ...
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Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is ...
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Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.
With the development of the Internet, users can freely publish posts on various social media platforms, which offers great convenience for keeping abreast of the world. However, posts usually carry many rumors, which ...
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With the development of the Internet, users can freely publish posts on various social media platforms, which offers great convenience for keeping abreast of the world. However, posts usually carry many rumors, which require plenty of manpower for monitoring. Owing to the success of modern machine learning techniques, especially deep learning models, we tried to detect rumors as a classification problem automatically. Early attempts have always focused on building classifiers relying on image or text information, i.e., single modality in posts. Thereafter, several multimodal detection approaches employ an early or late fusion operator for aggregating multiple source information. Nevertheless, they only take advantage of multimodal embeddings for fusion and ignore another important detection factor, i.e., the intermodal inconsistency between modalities. To solve this problem, we develop a novel deep visual-linguistic fusion network(DVLFN) considering cross-modal inconsistency, which detects rumors by comprehensively considering modal aggregation and contrast information. Specifically, the DVLFN first utilizes visual and textual deep encoders, i.e., Faster R-CNN and bidirectional encoder representations from transformers, to extract global and regional embeddings for image and text modalities. Then, it predicts posts' authenticity from two aspects:(1) intermodal inconsistency, which employs the Wasserstein distance to efficiently measure the similarity between regional embeddings of different modalities, and(2) modal aggregation, which experimentally employs the early fusion to aggregate two modal embeddings for prediction. Consequently, the DVLFN can compose the final prediction based on the modal fusion and inconsistency measure. Experiments are conducted on three real-world multimedia rumor detection datasets collected from Reddit, Good News, and Weibo. The results validate the superior performance of the proposed DVLFN.
作者：
Zabian, ArwaJadara University
Faculty of Science and Information Technology Department of Software Engineering Irbid Jordan
The speed at which the data is generated, processed and stored to meet the demands of our lives today requires new technologies for handling and using this amount of data. Research on the effective usage of this data ...
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Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling e...
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Dynamic software update(DSU)patches programs on the *** often involves the critical task of object transformation that converts live objects of the old-version program to their semantically consistent counterparts und...
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Dynamic software update(DSU)patches programs on the *** often involves the critical task of object transformation that converts live objects of the old-version program to their semantically consistent counterparts under the new-version *** task is accomplished by invoking an object transformer on each stale ***,a defective transformer failing to maintain consistency would cause errors or even crash the *** propose TOAST(Test Object trAnSformaTion),an automated approach to detecting potential inconsistency caused by object *** first analyzes an update to identify multiple target methods and then adopts a fuzzer with specially designed inconsistency guidance to randomly generate object states to drive two versions of a target *** creates two corresponding execution traces and a pair of old and new *** finally performs object transformation to create a transformed object and detects inconsistency between it and the corresponding new object produced from scratch by the new ***,TOAST checks behavior inconsistency by comparing the return variables and exceptions of the two *** evaluation on 130 updates with default transformers shows that TOAST is promising:it got 96.0%precision and 85.7%recall in state inconsistency detection,and 81.4%precision and 94.6%recall in behavior inconsistency *** inconsistency guidance improved the fuzzing efficiency by 14.1%for state inconsistency detection and 40.5%for behavior inconsistency detection.
Due to self-occlusion and high degree of freedom, estimating 3D hand pose from a single RGB image is a great challenging problem. Graph convolutional networks (GCNs) use graphs to describe the physical connection rela...
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Quantum pseudorandom state generators (PRSGs) have stimulated exciting developments in recent years. A PRSG, on a fixed initial (e.g., all-zero) state, produces an output state that is computationally indistinguishabl...
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