Adaptive software systems can assist in reducing food waste and improving food safety by enabling a real-time quality assessment. Such systems include sensors to monitor the food's condition and machine learning-b...
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An embedded anisotropic thin layer model, based on the extended subgridding boundary condition (SGBC) technique, has been developed and integrated into the massively parallel computer codes JEMS-FDTD for efficient sim...
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This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations bet...
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This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations between them. Most DCs consist of a consecutive words (eg. as a result); however paired DCs are composed of non-consecutive words that together signal the discourse relation (eg. on one hand … on the other hand). Although paired DCs are not common in English, they are very frequent in Chinese. The contribution of this paper in two-fold: First, we propose a methodology for the automatic identification of Chinese paired DCs. Second, we present a new corpus based on the Chinese Discourse Treebank (CDTB) [1] annotated with paired DCs. To identify paired DCs, we experimented with two main approaches: hypothesis testing and supervised machine learning. Although the hypothesis testing approaches led to lower than expected results, the simple machine learning models achieved F-scores between 72.5%–75.6% with no fine-tuning.
While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),...
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While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT *** an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security *** contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this ***2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT *** also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT ***,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT *** evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its *** an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%***,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are *** proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.
Pre-trained vision-language models (VLMs) have shown impressive results in various visual classification tasks. However, we often fail to fully unleash their potential when adapting them for new concept understanding ...
With the continuous development of intelligent connected vehicle industry, cameras and other vehicle-mounted devices are widely used, so the amount of data collection is increasing. There is a large amount of sensitiv...
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Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the...
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Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be ***-learning,a promising technique for leveraging data from previou...
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Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be ***-learning,a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks,has been a popular way to tackle this ***,the existing meta-learning models have been evidenced to be overfitting when the meta-training tasks are *** overcome this challenge,we present a novel self-supervised task augmentation with meta-learning framework,namely ***,we introduce the task augmentation,which explores two different strategies and combines them to extend meta-training ***,we devise two auxiliary losses for integrating self-supervised learning into meta-learning to learn more generalizable and transferable *** results show that STAM can achieve consistent and considerable performance improvement to existing state-of-the-art methods on four datasets.
Uniform Interpolation (UI) is an advanced reasoning service used to narrow down an ontology to a restricted view. This new ontology, known as a uniform interpolant, will only consist of the ''relevant names...
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B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to t...
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