Although traditional research methods for intrusion detection can effectively prevent and mitigate issues such as data leaks to avoid severe consequences, existing intrusion detection technologies encounter limitation...
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Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by e...
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Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.
Monitoring respiration is an important component of personal health *** recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-based mul...
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Monitoring respiration is an important component of personal health *** recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring,existing proposals for Wi-Fi-based mul-ti-person respiration sensing mainly extract individual's respiration rate in the frequency domain using the fast Fourier transform(FFT)or multiple signal classification(MUSIC)method,leading to the following limitations:1)largely ineffec-tive in recovering breaths of multiple persons from received mixed signals and in differentiating individual breaths,2)un-able to acquire the time-varying respiration pattern when the subject has respiratory abnormity,such as apnea and chang-ing respiration rates,and 3)difficult to identify the real number of subjects when multiple subjects share the same or simi-lar respiration *** address these issues,we propose Wi-Fi-enabled MUlti-person SEnsing(WiMUSE)as a signal pro-cessing pipeline to perform respiration monitoring for multiple persons ***,as a pioneering time domain approach,WiMUSE models the mixed signals of multi-person respiration as a linear superposition of multiple waveforms,so as to form a blind source separation(BSS)*** effective separation of the signal sources(respira-tory waveforms)further enables us to quantify the differences in the respiratory waveform patterns of multiple subjects,and thus to identify the number of subjects along with their respective respiration *** implement WiMUSE on commodity Wi-Fi devices and conduct extensive experiments to demonstrate that,compared with the approaches based on the FFT or MUSIC method,90%error of respiration rate can be reduced by more than 60%.
Knowledge of medication and disease has been rapidly accumulated. Also, an increasing number of researchers have paid more attention to predicting medicine-disease associations by machine learning methods. The associa...
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Unmanned Aerial Vehicle (UAV) crowdsensing, as a complement to Mobile Crowdsensing (MCS), can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this paper, w...
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Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection bet...
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Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.
Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category...
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Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.
In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, spee...
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Code semantic learning serves as the basis of many program analysis tasks. Researchers have paid much effort to build robust and effective code representation models over the years. One line of work focuses on introdu...
Code semantic learning serves as the basis of many program analysis tasks. Researchers have paid much effort to build robust and effective code representation models over the years. One line of work focuses on introducing the code structure into the representations. To further improve the robustness of the code representation, approaches based on compiler intermediate representations(IRs) are proposed. However, these IR-based models suffer from heavy computational costs and memory overhead. How to represent program semantics effectively and efficiently still remains a challenge. To this end, we propose EECS, an effective and efficient code semantic representation approach based on compiler IRs and a hybrid attention mechanism. For input representation, to address the unlimited vocabulary size issue in IR, we propose a variable identification strategy to allocate each register variable to a new ID that can represent their relative positions. Besides, we also extract the data flow information among the code blocks. Then we build a hierarchical multi-layer Transformer encoder to capture the data dependency information as well as the code semantics through a hybrid attention mechanism. To enable EECS to learn code semantics and functionality better, we optimize three objectives jointly during the training *** results on three code semantic understanding tasks show that EECS performs better than the state-of-the-art techniques, demonstrating the remarkable capability of EECS on program semantics understanding.
A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engi...
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A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.
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