scholarly journals TEP2MP: A text-emotion prediction model oriented to multi-participant text-conversation scenario with hybrid attention enhancement

2022 ◽  
Vol 19 (3) ◽  
pp. 2671-2699
Author(s):  
Huan Rong ◽  
◽  
Tinghuai Ma ◽  
Xinyu Cao ◽  
Xin Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Online Social Networks ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Text Representation ◽  
Search Mechanism ◽  
Proposed Model ◽  
The Government ◽  
Prediction Efficiency ◽  
Target Speaker

<abstract> <p>With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an <italic>affective space mapping</italic> is constructed, which represents the original conversation-text as an n-dimensional <italic>affective vector</italic> so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.</p> </abstract>

scholarly journals A Novel Hybrid Model for Cantonese Rumor Detection on Twitter

2020 ◽  
Vol 10 (20) ◽  
pp. 7093
Author(s):  
Xinyu Chen ◽  
Liang Ke ◽  
Zhipeng Lu ◽  
Hanjian Su ◽  
Haizhou Wang
Keyword(s):  
Hybrid Model ◽  
Online Social Networks ◽  
Social Order ◽  
Attention Mechanism ◽  
Mobile Internet ◽  
Semantic Features ◽  
Proposed Model ◽  
Internal Structures ◽  
Rumor Detection ◽  
Future Work

The development of information technology and mobile Internet has spawned the prosperity of online social networks. As the world’s largest microblogging platform, Twitter is popular among people all over the world. However, as the number of users on Twitter increases, rumors have become a serious problem. Therefore, rumor detection is necessary since it can prevent unverified information from causing public panic and disrupting social order. Cantonese is a widely used language in China. However, to the best of our knowledge, little research has been done on Cantonese rumor detection. In this paper, we propose a novel hybrid model XGA (namely XLNet-based Bidirectional Gated Recurrent Unit (BiGRU) network with Attention mechanism) for Cantonese rumor detection on Twitter. Specifically, we take advantage of both semantic and sentiment features for detection. First of all, XLNet is employed to produce text-based and sentiment-based embeddings at the character level. Then we perform joint learning of character and word embeddings to obtain the words’ external contexts and internal structures. In addition, we leverage BiGRU and the attention mechanism to obtain important semantic features and use the Cantonese rumor dataset we constructed to train our proposed model. The experimental results show that the XGA model outperforms the other popular models in Cantonese rumor detection. The research in this paper provides methods and ideas for future work in Cantonese rumor detection on other social networking platforms.

Generative Text Steganography Based on LSTM Network and Attention Mechanism with Keywords

2020 ◽  
Vol 2020 (4) ◽  
pp. 291-1-291-8
Author(s):  
Huixian Kang ◽  
Hanzhou Wu ◽  
Xinpeng Zhang
Keyword(s):  
Online Social Networks ◽  
Short Term Memory ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Short Term ◽  
Secret Data ◽  
Text Steganography ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
The Given

The widespread use of text over online social networks makes it quite suitable for steganography. Conventional text steganography usually transmits the secret data by either slightly modifying the given text or hiding the secret data through synonym replacement. The rapid development of deep neural networks (DNNs) has led automatically generating the steganographic text to become an important and promising topic. This has motivated us to propose a novel generative text steganographic method based on long short-term memory (LSTM) network in this paper. We apply attention mechanism with keywords to the LSTM network to generate the steganographic text. Experiments show that, compared to the related work, the steganographic text generated by the proposed method is of higher semantic quality and more capable of resisting against steganalysis, which has shown the superiority.

scholarly journals HAC: Hybrid Access Control for Online Social Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Fangfang Shan ◽  
Hui Li ◽  
Fenghua Li ◽  
Yunchuan Guo ◽  
Ben Niu
Keyword(s):  
Access Control ◽  
Mobile Networks ◽  
Online Social Networks ◽  
Rapid Development ◽  
Prototype System ◽  
Proposed Model ◽  
Control Schemes ◽  
Policy Specification ◽  
Hybrid Access ◽  
Network Technologies

The rapid development of communication and network technologies including mobile networks and GPS presents new characteristics of OSNs. These new characteristics pose extra requirements on the access control schemes of OSNs, which cannot be satisfied by relationship-based access control currently. In this paper, we propose a hybrid access control model (HAC) which leverages attributes and relationships to control access to resources. A new policy specification language is developed to define policies considering the relationships and attributes of users. A path checking algorithm is proposed to figure out whether paths between two users can fit in with the hybrid policy. We develop a prototype system and demonstrate the feasibility of the proposed model.

scholarly journals NTAM-LSTM models of network traffic prediction

2022 ◽  
Vol 355 ◽  
pp. 02007
Author(s):  
Jihong Zhao ◽  
Xiaoyuan He
Keyword(s):  
Prediction Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Accurate Prediction ◽  
Traffic Prediction ◽  
Network Resources ◽  
Multiple Characteristics ◽  
Lstm Network

Accurate prediction of network traffic is very important in allocating network resources. With the rapid development of network technology, network traffic becomes more complex and diverse. The traditional network traffic prediction model cannot accurately predict the current network traffic within the effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long and Short Time Memory. Firstly, the model preprocesses the historical dataset of network traffic with multiple characteristics. Then the LSTM network is used to make initial prediction for the processed dataset. Finally, attention mechanism is introduced to get more accurate prediction results. Compared with other network traffic prediction models, NTAM-LSTM prediction model can achieve higher prediction accuracy and take shorter running time.

EFFECT OF DIGITAL COMMUNITY TOWARDS DEMOCRACY PROCESS: DIGITAL PARTISIPATION ANALYSIS IN INDONESIA

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Alih Aji Nugroho
Keyword(s):  
Public Policy ◽  
Social Media ◽  
Political Participation ◽  
Public Space ◽  
Rapid Development ◽  
Critical Mass ◽  
Digital Society ◽  
Digital World ◽  
Digital Community ◽  
The Government

The world is entering a new phase of the digital era, including Indonesia. The unification of the real world and cyberspace is a sign, where the conditions of both can influence each other (Hyung Jun, 2018). The patterns of behavior and public relations in the virtual universe gave rise to new social interactions called the Digital Society. One part of Global Megatrends has also influenced public policy in Indonesia in recent years. Critical mass previously carried out conventionally is now a virtual movement. War of hashtags, petitions, and digital community comments are new tools and strategies for influencing policy. This paper attempts to analyze the extent of digital society's influence on public policy in Indonesia. As well as what public policy models are needed. Methodology used in this analysis is qualitative descriptive. Data collection through literature studies by critical mass digital recognition in Indonesia and trying to find a relationship between political participation through social media and democracy. By processing the pro and contra views regarding the selection of social media as a level of participation, this paper finds that there are overlapping interests that have the potential to distort the articulation of freedom of opinion and participation. - which is characteristic of a democratic state. The result is the rapid development of digital society which greatly influences the public policy process. Digital society imagines being able to participate formally in influencing policy in Indonesia. The democracy that developed in the digital society is cyberdemocracy. Public space in the digital world must be guaranteed security and its impact on the policies that will be determined. The recommendation given to the government is that a cyber data analyst is needed to oversee the issues that are developing in the digital world. Regulations related to the security of digital public spaces must be maximized. The government maximizes cooperation with related stakeholders.Keywords: Digital Society; Democracy; Public policy; Political Participation

scholarly journals Mapping the Sharing Economy in China

2020 ◽  
Vol 12 (16) ◽  
pp. 6333
Author(s):  
Chan Liu ◽  
Raymond K. H. Chan ◽  
Maofu Wang ◽  
Zhe Yang
Keyword(s):  
Global Economy ◽  
Rapid Development ◽  
Sharing Economy ◽  
Essential Elements ◽  
Mobile Internet ◽  
Internet Technology ◽  
Short Term ◽  
Bona Fide ◽  
The Government ◽  
Idle Capacity

Harnessing the rapid development of mobile internet technology, the sharing economy has experienced unprecedented growth in the global economy, especially in China. Likely due to its increasing popularity, more and more businesses have adopted this label in China. There is a concern as to the essential meaning of the sharing economy. As it is difficult to have a universally accepted definition, we aim to map the sharing economy and demystify the use of it in China in this paper. We propose seven organizing essential elements of the sharing economy: access use rights instead of ownership, idle capacity, short term, peer-to-peer, Internet platforms mediated, for monetary profit, and shared value orientation. By satisfying all or only parts of these elements, we propose one typology of sharing economy, and to differentiate bona fide sharing economy from quasi- and pseudo-sharing economy. Finally, there are still many problems that need to be solved urgently in the real sharing economy from the perspective of the government, companies and individuals.

scholarly journals Evaluation of China’s Circular Agriculture Performance and Analysis of the Driving Factors

2021 ◽  
Vol 13 (4) ◽  
pp. 1643
Author(s):  
Biao Li ◽  
Yunting Feng ◽  
Xiqiang Xia ◽  
Mengjie Feng
Keyword(s):  
Rapid Development ◽  
Arable Land ◽  
Indicator System ◽  
Resource Scarcity ◽  
Entropy Method ◽  
Agricultural Industry ◽  
Evaluation Indicator ◽  
Evaluation Indicator System ◽  
Per Capita ◽  
The Government

Along with industry upgrading and urbanization, the agricultural industry in China has been experiencing a stage of rapid development, on the bright side. On the other side, ecological environment deterioration and resource scarcity have become prevalent. Called by the current situation, circular agriculture arises as a direction for the industry to achieve sustainable development. This study develops an evaluation indicator system for circular agriculture using an entropy method, and evaluates factors that could drive the Chinese agricultural industry to achieve better performance. We employ the method using provincial data collected from the province of Henan, in which around 10% of the total grain in China is produced. It was found that agricultural technology and water resources per capita are positively related to circular performance in agriculture. In contrast, urbanization and arable land per capita are negatively related to circular performance. This article provides support to the government in policy-making related to the improvement of circular agricultural performance.

scholarly journals An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Said Gounane ◽  
Yassir Barkouch ◽  
Abdelghafour Atlas ◽  
Mostafa Bendahmane ◽  
Fahd Karami ◽  
...  
Keyword(s):  
Mathematical Theory ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Threshold ◽  
Social Distancing ◽  
Sir Epidemic ◽  
Proposed Model ◽  
Disease Spreading ◽  
The Government ◽  
The Impact

Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

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