scholarly journals PREDICTING TRUST IN A SOCIAL NETWORK BASED ON STRUCTURAL SIMILARITIES USING A MULTI-LAYERED PERCEPTRON NEURAL NETWORK

2021 ◽  
Vol 22 (1) ◽  
pp. 103-117
Author(s):  
Amir Hossein Danesh ◽  
Hossein Shirgahi
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Network ◽  
Previous History ◽  
Structural Similarity ◽  
Training Data ◽  
Negative Effects ◽  
Data Set ◽  
Trained Neural Network ◽  
The Web

Although research on social networks is progressing rapidly, the positive and negative effects of this area should be evaluated. One of the problems is that social networks are very broad and anyone can have influence on them. This matter can cause the issue of people with different beliefs. Therefore, determining the amount of trust to various resources on social networks, and especially resources for which there is no previous history on the web, is one of the main challenges in this field. In this paper, we present a method for predicting trust in a social network by structural similarities through the neural network. In this method, the web of trust data set is converted to a structural similarity data set based on the similarity of the trustors and trustees first. Then, on the created data set, a part of the data set is considered as the training data and it is trained based on the multilayer perceptron neural network and then the trained neural network is tested based on the test data. In the proposed method, the MSE value is less than 0.01, which has improved more than 0.02 compared to previous methods. Based on the obtained results, the proposed method has provided acceptable accuracy. ABSTRAK: Walaupun kajian tentang rangkaian sosial adalah sangat pesat, kesan positif dan negatif dalam ruang lingkup ini perlu dinilai. Masalah rangkaian sosial adalah sangat luas dan sesiapa sahaja boleh terpengaruh. Perkara ini akan menyebabkan manusia dengan pelbagai isu kepercayaan. Oleh itu, menentukan nilai kepercayaan melalui pelbagai sumber dalam rangkaian sosial, terutama sumber-sumber yang tidak mempunyai sejarah lepas dalam web, adalah salah satu cabaran dalam bidang ini. Kajian ini membentangkan jangkaan kepercayaan dalam rangkaian sosial melalui persamaan struktur dengan menggunakan rangkaian neural. Kaedah ini ditentukan dengan menukar set data web kepercayaan kepada struktur set data hampir sama berdasarkan kesamaan pemegang dan pemberi amanah. Kemudian, sebilangan set data yang telah dibina ini dipertimbangkan sebagai data latihan dan ia dilatih berdasarkan rangkaian neural perseptron berbagai lapisan dan kemudian rangkaian neural yang terlatih ini diuji berdasarkan data ujian. Dalam kaedah yang dicadangkan ini, nilai MSE adalah kurang daripada 0.01, di mana telah diperbaiki kepada 0.02 lebih daripada kaedah-kaedah sebelum ini. Berdasarkan dapatan kajian, didapati kaedah yang dicadangkan ini menunjukkan ketepatan yang boleh diterima.

Crack-Depth Determination by a Neural Network with a Synthetic Training Data Set

1993 ◽  
pp. 803-810 ◽  
Author(s):  
M. Takadoya ◽  
M. Notake ◽  
M. Kitahara ◽  
J. D. Achenbach ◽  
Q. C. Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Crack Depth ◽  
Training Data ◽  
Data Set ◽  
Depth Determination ◽  
Synthetic Training Data

scholarly journals Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Data Set ◽  
Set Size ◽  
The Neural Network ◽  
Surface Character ◽  
Finite State ◽  
Character Sequences ◽  
Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

scholarly journals Academic Social Networks: How the web is changing our way to make and communicate researches

2015 ◽  
Vol 7 (2) ◽  
pp. 3-14 ◽  
Author(s):  
Giovanni Bonaiuti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Sites ◽  
Rapid Evolution ◽  
Scientific Field ◽  
Academic Social Network ◽  
Academic Social Networks ◽  
Insight Into ◽  
The Web ◽  
Natural Component

Abstract Networking is not only essential for success in academia, but it should also be seen as a natural component of the scholarly profession. Research is typically not a purely individualistic enterprise. Academic social network sites give researchers the ability to publicise their research outputs and connect with each other. This work aims to investigate the use done by Italian scholars of 11/D2 scientific field. The picture presented shows a realistic insight into the Italian situation, although since the phenomenon is in rapid evolution results are not stable and generalizable.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

Combining Self-supervised Learning and Active Learning for Disfluency Detection

2022 ◽  
Vol 21 (3) ◽  
pp. 1-25
Author(s):  
Shaolei Wang ◽  
Zhongyuan Wang ◽  
Wanxiang Che ◽  
Sendong Zhao ◽  
Ting Liu
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
Training Data ◽  
Fine Tuning ◽  
Training Dataset ◽  
Performance Gap ◽  
Annotation Costs ◽  
Trained Neural Network

Spoken language is fundamentally different from the written language in that it contains frequent disfluencies or parts of an utterance that are corrected by the speaker. Disfluency detection (removing these disfluencies) is desirable to clean the input for use in downstream NLP tasks. Most existing approaches to disfluency detection heavily rely on human-annotated data, which is scarce and expensive to obtain in practice. To tackle the training data bottleneck, in this work, we investigate methods for combining self-supervised learning and active learning for disfluency detection. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled data and propose two self-supervised pre-training tasks: (i) a tagging task to detect the added noisy words and (ii) sentence classification to distinguish original sentences from grammatically incorrect sentences. We then combine these two tasks to jointly pre-train a neural network. The pre-trained neural network is then fine-tuned using human-annotated disfluency detection training data. The self-supervised learning method can capture task-special knowledge for disfluency detection and achieve better performance when fine-tuning on a small annotated dataset compared to other supervised methods. However, limited in that the pseudo training data are generated based on simple heuristics and cannot fully cover all the disfluency patterns, there is still a performance gap compared to the supervised models trained on the full training dataset. We further explore how to bridge the performance gap by integrating active learning during the fine-tuning process. Active learning strives to reduce annotation costs by choosing the most critical examples to label and can address the weakness of self-supervised learning with a small annotated dataset. We show that by combining self-supervised learning with active learning, our model is able to match state-of-the-art performance with just about 10% of the original training data on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.

Patterns of frequent user interactions in blogosphere

2020 ◽  
Author(s):  
Krzysztof Rudek ◽  
Jarosław Koźlak
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Relative Difference ◽  
Pattern Identification ◽  
Frequent Patterns ◽  
Frequent User ◽  
User Interactions ◽  
Data Set ◽  
Political Issues

Abstract The aim of the paper is to identify and categorize frequent patterns describing interactions between users in social networks. We analyze a social network with relationships between users that evolve in time already identified. In our research, we discover patterns based on frequent interactions between groups of users. The patterns are described by the characteristics of these interactions, such as their reciprocity, or the relative difference between estimations of global influences of the users participating in the discussions. The modification of the apriori algorithms is applied as one of the methods for pattern identification. The analyzed social network is built using the data set containing data from the Polish blog website salon24, which concerns mostly socio-political issues.

scholarly journals Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
R. Manjula Devi ◽  
S. Kuppuswami ◽  
R. C. Suganthe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Model ◽  
Training Data ◽  
Experimental Result ◽  
Training Algorithms ◽  
Data Set ◽  
Training Time ◽  
Input Sample ◽  
Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

The Basic Control Chart Pattern Recognition Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 1042-1045
Author(s):  
Xu An Qiao ◽  
Jing Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Performance ◽  
Training Data ◽  
Data Set ◽  
Control Diagram ◽  
Recognition Success ◽  
Control Chart Pattern ◽  
Very High ◽  
Performance Pattern

The pattern recognition process control diagram, this paper puts forward a new method of training neural network. It only needs a small training data set can complete this work. This method is also compatible with the training algorithm, and get a better network performance. Pattern recognition success rate is very high in the larger parameter range, but also has some comparability.

IMPROVEMENT OF IMAGE CLASSIFICATION USING WAVELET COEFFICIENTS WITH STRUCTURED-BASED NEURAL NETWORK

2008 ◽  
Vol 18 (03) ◽  
pp. 195-205 ◽  
Author(s):  
WEIBAO ZOU ◽  
ZHERU CHI ◽  
KING CHUEN LO
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Image Classification ◽  
Dimensional Space ◽  
Training Data ◽  
Wavelet Coefficients ◽  
Classification Rate ◽  
Data Set ◽  
Low Dimensional ◽  
High Level

Image classification is a challenging problem in organizing a large image database. However, an effective method for such an objective is still under investigation. A method based on wavelet analysis to extract features for image classification is presented in this paper. After an image is decomposed by wavelet, the statistics of its features can be obtained by the distribution of histograms of wavelet coefficients, which are respectively projected onto two orthogonal axes, i.e., x and y directions. Therefore, the nodes of tree representation of images can be represented by the distribution. The high level features are described in low dimensional space including 16 attributes so that the computational complexity is significantly decreased. 2800 images derived from seven categories are used in experiments. Half of the images were used for training neural network and the other images used for testing. The features extracted by wavelet analysis and the conventional features are used in the experiments to prove the efficacy of the proposed method. The classification rate on the training data set with wavelet analysis is up to 91%, and the classification rate on the testing data set reaches 89%. Experimental results show that our proposed approach for image classification is more effective.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

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