This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often ...
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ISBN:
(数字)9798350358155
ISBN:
(纸本)9798350358162
This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages.
Sound Event Detection (SED) often employs pre-trained models to address data scarcity issues. However, existing systems usually treat the pretrained models as frozen feature extractors, resulting in suboptimal efficie...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Sound Event Detection (SED) often employs pre-trained models to address data scarcity issues. However, existing systems usually treat the pretrained models as frozen feature extractors, resulting in suboptimal efficiency, or fully fine-tune the pretrained models, which requires substantial computational resources. To fully leverage the knowledge from pretrained models, we propose a novel Global Enhanced Frame Prompt Tuning (GE-FPT) framework, providing global and local insights tailored for SED tasks. Additionally, Frame Prompt Tuning (FPT) is proposed in our GE-FPT to effectively explore local temporal information, i.e., temporal details and context, which is essential for SED tasks, and in particular, for precise event boundary detection. Extensive experiments claim that our approach significantly outperforms full fine-tuning methods while substantially reducing computational costs. Our system achieves new state-of-the-art results, with PSDS1/PSDS2 scores of 0.628/0.845 on the DCASE2023 Challenge Task4 dataset. The source code is publicly available 1 .
Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced Key Clues Selection (LE-KCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LE-KCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance.
Individuals allocate a substantial portion of their time within indoor environments, wherein the thermal conditions wield considerable influence over their well-being and efficacy across diverse domains. This study em...
Individuals allocate a substantial portion of their time within indoor environments, wherein the thermal conditions wield considerable influence over their well-being and efficacy across diverse domains. This study employs a suite of sensors, notably encompassing an MS1100 CO2 concentration level, a DHT 11 humidity sensor, and a Logitech web camera for visual data acquisition, to monitor the occupancy of enclosed spaces. Principal aims encompass discerning the optimal operating temperature, establishing a state of thermal comfort, and managing energy consumption, particularly pertaining to illumination. The determination of the optimal operational temperature relies on a specific model, while the delineation of the thermal comfort envelope adheres to the PMV methodology as delineated by the ASHRAE 55 standard, facilitated through the CBE tool. The secondary objective entails a dual consideration of room occupancy and C02 concentration, with fuzzy logic control (FLC) being employed to regulate lighting in response to fluctuations in occupancy and C02 levels. The investigation underscores that prudent management of thermal comfort yields enhancements in energy efficiency. Nonetheless, shortcomings in parameter configuration within the PMV methodology are acknowledged.
Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162...
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Utilizing randomized experiments to evaluate the effect of short-term treatments on the short-term outcomes has been well understood and become the golden standard in industrial practice. However, as service systems b...
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Text editing, i.e., the process of modifying or manipulating text, is a crucial step in human writing process. In this paper, we study the problem of controlled text editing by natural language instruction. According ...
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A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With t...
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ISBN:
(纸本)9781450396943
A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.
Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 ...
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Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.
Accurately determining the number of occupants in a room is crucial for optimizing smart environments and energy efficiency in HVAC systems. This paper presents a deep learning approach for precise, real-time classroo...
Accurately determining the number of occupants in a room is crucial for optimizing smart environments and energy efficiency in HVAC systems. This paper presents a deep learning approach for precise, real-time classroom occupancy estimation to facilitate smart HVAC control. Utilizing a YOLOv4 object detection model, trained on an extensive dataset of labeled human faces, we developed a robust computer vison model with OpenCV libraries This model performs facial recognition and occupant counting through live video feeds from a Logitech c20 camera, achieving over 98% accuracy in typical classroom settings. We investigate the different techniques to address challenges such as occlusion and variability. The integration of our occupancy estimation model with HVAC control systems underscores a significant stride towards achieving energy conservation and sustainability goals in educational institutions, aligning with the emerging paradigms of smart building management systems.
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