Ecological validity remains essential for generalizing scientific research into real-world applications. However, current methods for crowd emotion detection lack ecological validity due to limited diversity samples i...
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The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation *** their trans...
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The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation *** their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant *** challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy *** works often conflated safety issues with security *** contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of *** on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in ***,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of...
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With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism withou...
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With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism without prior ***,endogenous security lacks a scientific and formal definition in industrial ***,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost ***,the endogenous security innovation mechanism is clearly ***,an improved clone selection algorithm based on federated learning is ***,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge *** conduct identity authentication experiments based on two types of blockchains and compare their experimental *** on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial *** of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge ***,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.
The incorporation of neural networks into medical imaging has recently resulted in significant modifications to diagnosis. This article looks at the job of brain networks in clinical picture handling, featuring their ...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone...
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Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.
Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Lan...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
This paper represents that one of the critical subfields of NLP, SA applies DL techniques to analyze the feelings expressed in text, image, and voice context. As new technology of Web 2.0 emerges, there are many oppor...
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Over the past decades, integration of wireless sensor networks (WSNs) and computer vision (CV) technology has shown promising results in mitigating crop losses caused by wild animal attacks. Studies have demonstrated ...
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Over the past decades, integration of wireless sensor networks (WSNs) and computer vision (CV) technology has shown promising results in mitigating crop losses caused by wild animal attacks. Studies have demonstrated the effectiveness of these technologies in providing real-time monitoring and early detection of animal intrusions into agricultural fields. By deploying WSNs equipped with motion sensors and cameras, farmers can receive instant alerts when wild animals enter their fields, allowing for timely intervention to prevent crop damage. Furthermore, advancements in CV algorithms possess made possible to automatically detect and classify the animal species, facilitating targeted response strategies. For example, sophisticated image processing techniques can differentiate between harmless birds and destructive mammals, allowing farmers to focus their efforts on deterring the most damaging species. Field trials and pilot projects implementing WSN-CV systems have reported significant reductions in crop losses attributed to wild animal raids. By leveraging data collected through sensor networks and analyzed using computer vision algorithms, farmers can make informed decisions regarding pest and insect management strategies. This data-driven approach has led to more efficient utilization of resources, such as targeted application of insecticides and pesticides, resulting in both economic and environmental benefits. Moreover, the integration of WSN-CV technology has enabled the development of innovative deterrent systems that leverage artificial intelligence and automation. These systems can deploy non-lethal methods, such as sound or light-based repellents, to deter wild animals without causing harm to the environment or wildlife populations. Overall, the combination of wireless sensor networks and computer vision technology provides the promising resolution to the long-standing issue of wild animal-related losses in agriculture. By harnessing the power of data and a
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