With the popularity of online mental health platforms, more individuals are seeking help and receiving social support by openly discussing their problems. Therefore, it's crucial to gain a deeper understanding of ...
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With the popularity of online mental health platforms, more individuals are seeking help and receiving social support by openly discussing their problems. Therefore, it's crucial to gain a deeper understanding of which problem disclosures and social support on these platforms can attract more user attention and engagement. Previous research has primarily focused on social media forums. Our work concentrates on the professional mental health platform, intending to understand the linguistic features present in posts that promote user engagement and interaction. We employ text mining and deep learning techniques to analyze posts consisting of 22,250 questions from help-seekers and 78,328 answers providing social support extracted from the Chinese online mental health counseling platform. Initially, we analyze the high-frequency words and topics of the questions and answers to gain insights into the primary focal points and the range of topics covered in these posts. The results indicate that work-related issues are the most concerning and troublesome for help-seekers, and the topics that users follow are approximately 8 types, including growth, family, in-love, marriage, emotions, human-relations, behavioral-therapy and career. Subsequently, we analyze the language usage in question-and-answer posts with different engagement from three aspects: vocabulary categories, linguistic style matching, and language modeling, aiming to identify which linguistic features can attract more user attention and engagement. The results reveal that high-engagement answer posts exhibit a higher degree of linguistic style matching with the corresponding questions, and the use of vocabulary categories also influences the attention and engagement of the posts. By exploring the linguistic features and patterns displayed in posts with different levels of engagement on the professional online mental health platform, this study offers deep insights into user behavior and the factors that impact
Recent experiment has uncovered semimetal bismuth (Bi) as an excellent electrical contact to monolayer MoS2 with ultralow contact resistance. The contact physics of the broader semimetal/monolayer-semiconductor family...
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A data center is a cluster of servers, which is typically an organic collection of tens of thousands of servers. The sheer number of servers determines how well its performance is related to how it is interconnected. ...
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At present, unmanned aerial vehicles (UAVs) have been widely used in civilian fields (such as smart cities). However, the external environment of the UAV network is complex and computing resources are limited, so it i...
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ISBN:
(纸本)9781450387828
At present, unmanned aerial vehicles (UAVs) have been widely used in civilian fields (such as smart cities). However, the external environment of the UAV network is complex and computing resources are limited, so it is vulnerable to serious security threats, such as replay attacks, forgery attacks, man-in-the-middle attacks. Seriously, it will cause great damage to the work of the UAV in smart cities. Aiming at the problem of transmission instruction data leakage caused by malicious UAV in communication between road base stations and UAVs, this paper proposes a lightweight identity authentication scheme based on elliptic curve cryptography (ECC). The purpose is to ensure the identity authentication of the UAV and the road base station, to ensure that the mission instructions received by the UAV are authentic and reliable, and to ensure the privacy of the UAV's identity information. The algorithm mainly includes the system initialization phase, initializing the UAV and road base station, and the identity authentication phase. Compared with the traditional identity authentication method, this method has the characteristics of low computational cost, short key and high security, and is more suitable for UAV communication.
Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation s...
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ISBN:
(纸本)9781665438599
Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score.
For the high density of users and accompanying network service requirements in the cellular system, Device-to-Device (D2D) communication is a promising technology to cope with the increasing wireless traffic demands b...
ISBN:
(数字)9781728131061
ISBN:
(纸本)9781728131078
For the high density of users and accompanying network service requirements in the cellular system, Device-to-Device (D2D) communication is a promising technology to cope with the increasing wireless traffic demands by reusing spectrum resources. In practice, the wireless signal is easy to be eavesdropped in D2D communications underlaying cellular networks, hence, ensuring a secure communication for cellular user equipments (CUEs) is an urgent and meaningful problem. In this paper, we propose a joint subcarrier and power allocation scheme for maximizing the sum data rate of D2D pairs, meanwhile protecting the CUEs against eavesdropping. Specifically, in the proposed scheme, we first quantify the security performance with the secrecy data rate, and obtain the closed-form expression for the optimal power allocation of CUEs and D2D pairs by tightening the quality of service (QoS) and secrecy rate requirement constraints of CUEs. Based on the obtained power allocation solution, by searching the optimal mapping relationship between CUEs and D2D pairs, we develop a subcarrier assignment strategy with the Hungarian algorithm to solve it, which can further enhance the sum data rate of D2D pairs. Simulation results demonstrate that the proposed scheme can significantly yield better performance than other schemes.
Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dime...
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ISBN:
(纸本)9781665438599
Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input.
Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper prop...
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ISBN:
(纸本)9781665438599
Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance.
In recent years, group communication has become more and more popular. In the current data center network, multicast plays an extremely important role in group communication. Traditional data center network is designe...
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In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for opt...
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