scholarly journals Prediction of Hot Topics of Agricultural Public Opinion Based on Attention Mechanism LSTM Model

2021 ◽  
Vol 12 (4) ◽  
pp. 1-16
Author(s):  
Lifang Fu ◽  
Feifei Zhao
Keyword(s):  
Public Opinion ◽  
Short Term Memory ◽  
Classical Swine Fever ◽  
Attention Mechanism ◽  
Evaluation Indexes ◽  
The Public ◽  
Changing Trend ◽  
Lstm Network ◽  
Long Term Trend

In order to timely and accurately analyze the focus and appeal of public opinion on the Internet, A LSTM-ATTN model was proposed to extract the hot topics and predict their changing trend based on tens of thousands of news and commentary messages. First, an improved LDA model was used to extract hot words and classify the hot topics. Aimed to more accurately describe the detailed characteristics and long-term trend of topic popularity, a prediction model is proposed based on attention mechanism Long Short-Term Memory (LSTM) network, which named LSTM-ATTN model. A large number of numerical experiments were carried out using the public opinion information of "African classical swine fever" event in China. According to results of evaluation indexes, the relative superiority of LSTM-ATTN model was demonstrated. It can capture and reflect the inherent characteristics and periodic fluctuations of the agricultural public opinion information. Also, it has higher convergence efficiency and prediction accuracy.

Predicting Outcomes of Business Process Executions Based on LSTM Neural Networks and Attention Mechanism

2021 ◽  
Author(s):  
Jiaojiao Wang ◽  
Dongjin Yu ◽  
Chengfei Liu ◽  
Xiaoxiao Sun
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Sequential Data ◽  
Time Prediction ◽  
Prediction Time ◽  
Highly Sensitive ◽  
Long Short Term Memory ◽  
Early Decision ◽  
Lstm Network ◽  
Hidden Layer

Abstract To effectively predict the outcome of an on-going process instance helps make an early decision, which plays an important role in so-called predictive process monitoring. Existing methods in this field are tailor-made for some empirical operations such as the prefix extraction, clustering, and encoding, leading that their relative accuracy is highly sensitive to the dataset. Moreover, they have limitations in real-time prediction applications due to the lengthy prediction time. Since Long Short-term Memory (LSTM) neural network provides a high precision in the prediction of sequential data in several areas, this paper investigates LSTM and its enhancements and proposes three different approaches to build more effective and efficient models for outcome prediction. The first move on enhancement is that we combine the original LSTM network from two directions, forward and backward, to capture more features from the completed cases. The second move on enhancement is that we add attention mechanism after extracting features in the hidden layer of LSTM network to distinct them from their attention weight. A series of extensive experiments are evaluated on twelve real datasets when comparing with other approaches. The results show that our approaches outperform the state-of-the-art ones in terms of prediction effectiveness and time performance.

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

scholarly journals Responsibilization and Sexual Stigma Under Austerity: Surveying Public Support for Government-Funded PrEP in England

2019 ◽  
Vol 17 (4) ◽  
pp. 643-653
Author(s):  
Timothy Hildebrandt ◽  
Leticia Bode ◽  
Jessica S. C. Ng
Keyword(s):  
Public Opinion ◽  
Public Attitudes ◽  
Public Support ◽  
Policy Implications ◽  
The Public ◽  
Sexual Stigma ◽  
The Government ◽  
Original Survey ◽  
Media Narratives

Abstract Introduction Under austerity, governments shift responsibilities for social welfare to individuals. Such responsibilization can be intertwined with pre-existing social stigmas, with sexually stigmatized individuals blamed more for health problems due to “irresponsible” sexual behavior. To understand how sexual stigma affects attitudes on government healthcare expenditures, we examine public support for government-provisioned PrEP in England at a time when media narratives cast the drug as an expensive benefit for a small, irresponsible social group and the National Health Service’s long-term sustainability was in doubt. Methods This paper uses data from an original survey (N = 738) conducted in September 2016, when public opinion should be most sensitive to sexual stigma. A survey experiment tests how the way beneficiaries of PrEP were described affected support for NHS provision of it. Contrary to expectations, we found that support was high (mean = 3.86 on a scale of 1 to 5) irrespective of language used or beneficiary group mentioned. Differences between conditions were negligible. Discussion Sexual stigma does not diminish support for government-funded PrEP, which may be due to reverence for the NHS; resistance to responsibilization generally; or just to HIV, with the public influenced by sympathy and counter-messaging. Social policy implications Having misjudged public attitudes, it may be difficult for the government to continue to justify not funding PrEP; the political rationale for contracting out its provision is unnecessary and flawed. With public opinion resilient to responsibilization narratives and sexual stigma even under austerity, welfare retrenchment may be more difficult than social policymakers presume.

Intelligent Learning Model of Financial Spoken English Teaching based on BPTT algorithm and LSTM Network Model

2020 ◽  
Vol 39 (4) ◽  
pp. 4835-4846
Author(s):  
Han He ◽  
Si Yi ◽  
Weiwei Liu
Keyword(s):  
Speech Recognition ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Practical Significance ◽  
Long Term Memory ◽  
Term Memory ◽  
Error Elimination ◽  
Android Platform ◽  
Lstm Network

It is of great research value and practical significance to use new technology to improve the accuracy of English speech recognition and apply the system to mobile platforms for users to use. The main content of this paper is the long-term and short-term memory, and the current decoding part is applied to the Android platform, and the performance of the program is analyzed. Neural networks converge slowly, making learning long-term memory difficult. In the experiment, the BPTT algorithm is used to analyze the problem of error elimination in traditional recursive networks. Combining BPTT algorithm in LSTM network to solve the problem of traditional error elimination and improve speech recognition rate. In addition, this paper uses a new LSTM recurrent neural network to study the implementation of LSTM network on Android platform. Finally, this paper designs a comparative experiment to analyze the efficiency of oral English recognition. The results show that the research algorithm of this paper has certain effects.

IV. Finance and Politics in Urban Local Government in England, 1835–1900

1963 ◽  
Vol 6 (2) ◽  
pp. 212-225 ◽  
Author(s):  
E. P. Hennock
Keyword(s):  
Urban Communities ◽  
Urban Areas ◽  
Total Population ◽  
Term Trend ◽  
The Public ◽  
History Of ◽  
Urban Local Government ◽  
Urban Conditions ◽  
Long Term Trend

One of the chief features of the history of nineteenth-century England was undoubtedly the increase in the size of cities, and in the proportion of the total population who lived under urban conditions. Since this process turned out to be a long-term trend, the urban communities, especially the larger ones, were always historically more important than the statistics of urban to rural population in any one decade would have suggested.2 They were the growing points of the new society, and decisions taken there were to be of cumulative significance far beyond the borough boundary. The problems of the towns in any one generation became increasingly the problems of the nation in the next. For instance, it was assumed in 1848 that the administrative measures under the Public Health Act of that year were applicable to urban areas only. By 1872 it had been realized that they would have to be extended to the country as a whole.

scholarly journals Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning

Telecom ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 446-471
Author(s):  
Percy Kapadia ◽  
Boon-Chong Seet
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Access Network ◽  
Edge Computing ◽  
Fifth Generation ◽  
Radio Access ◽  
Ping Pong ◽  
Lstm Network ◽  
Multi Access

This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.

scholarly journals LSTM Networks for Online Cross-Network Recommendations

2018 ◽  
Author(s):  
Dilruk Perera ◽  
Roger Zimmermann
Keyword(s):  
Short Term Memory ◽  
Auxiliary Information ◽  
User Preference ◽  
Time Intervals ◽  
User Interactions ◽  
Linear Relationships ◽  
Proposed Model ◽  
Target Network ◽  
Lstm Network

Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues. The proposed model contains three main extensions to the standard LSTM: First, an attention gated mechanism to capture long-term user preference changes. Second, a higher order interaction layer to alleviate data sparsity. Third, time aware LSTM cell gates to capture irregular time intervals between user interactions. We illustrate our solution using auxiliary information from Twitter and Google Plus to improve recommendations on YouTube. Extensive experiments show that the proposed model consistently outperforms state-of-the-art in terms of accuracy, diversity and novelty.

Truncated attention mechanism and cascade loss for cross-modal person re-identification

2021 ◽  
pp. 1-13
Author(s):  
Shuo Shi ◽  
Changwei Huo ◽  
Yingchun Guo ◽  
Stephen Lean ◽  
Gang Yan ◽  
...  
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Principal Component ◽  
Image Features ◽  
Attention Mechanism ◽  
Text And Image ◽  
Level Information ◽  
Text Features ◽  
Lstm Network ◽  
Language Description

Person re-identification with natural language description is a process of retrieving the corresponding person’s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms.

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 A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dongguo Zhou ◽  
Yangjie Wu ◽  
Hong Zhou
Keyword(s):  
Evaluation Method ◽  
Feature Matching ◽  
Short Term Memory ◽  
Load Identification ◽  
Short Term ◽  
Monitoring Method ◽  
Term Memory ◽  
Load Monitoring ◽  
Lstm Network

Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. Finally, precision (P), recall (R), accuracy (A), and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances.

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