scholarly journals Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

2020 ◽  
Vol 2 (1) ◽  
pp. 126-165
Author(s):  
Alexey Yu. Bykovsky
Keyword(s):  
Fuzzy Logic ◽  
Quantum Optics ◽  
Heterogeneous Network ◽  
Network Architecture ◽  
Dimensional Space ◽  
Boolean Logic ◽  
Data Sets ◽  
Coding Schemes ◽  
Multiple Valued Logic ◽  
Robotic Devices

Quantum optics is regarded as the acknowledged method to provide network quantum keys distribution and in the future secure distributed quantum computing, but it should also provide cryptography protection for mobile robots and the Internet of Things (IoT). This task requires the design of new secret coding schemes, which can be also based on multiple-valued logic (MVL). However, this very specific logic model reveals new possibilities for the hierarchical data clustering of arbitrary data sets. The minimization of multiple-valued logic functions is proposed for the analysis of aggregated objects, which is possible for an arbitrary number of variables. In order to use all the useful properties of the multiple-valued logic, the heterogeneous network architecture is proposed, which includes three allocated levels of artificial intelligence (AI) logic modeling for discrete multiple-valued logic, Boolean logic, and fuzzy logic. Multiple-valued logic is regarded as the possible platform for additional secret coding, data aggregation, and communications, which are provided by the united high dimensional space for network addressing and the targeted control of robotic devices. Models of Boolean and fuzzy logic are regarded as separate logic levels in order to simplify the integration of various algorithms and provide control of additional data protection means for robotic agents.

Causality is Logically Definable

2011 ◽  
pp. 363-394
Keyword(s):  
Fuzzy Logic ◽  
Quantum Entanglement ◽  
Paraconsistent Logic ◽  
Dynamic Logic ◽  
Boolean Logic ◽  
Major Research ◽  
Research Topics ◽  
Intuitionist Logic ◽  
Multiple Valued Logic ◽  
And Mathematics

This is the conclusion chapter. Bertrand Russell’s view on logic and mathematics is briefly reviewed. An enjoyable debate on bipolarity and isomorphism is presented. Some historical facts related to YinYang are discussed. Distinctions are drawn between BDL from established logical paradigms including Boolean logic, fuzzy logic, multiple-valued logic, truth-based dynamic logic, intuitionist logic, paraconsistent logic, and other systems. Some major comments from critics on related works are answered. A list of major research topics is enumerated. The ubiquitous effects of YinYang bipolar quantum entanglement are summarized. Limitations of this work are identified. Some conclusions are drawn.

Breast Cancer Recognition Algorithm Based on Convolution Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 672-680
Author(s):  
Jiafu Jiang ◽  
Xinpei Li ◽  
Jin Wang ◽  
Se-Jung Lim
Keyword(s):  
Fuzzy Logic ◽  
Deep Learning ◽  
Network Architecture ◽  
Medical Images ◽  
Recognition Algorithm ◽  
Natural Images ◽  
Data Sets ◽  
Medical Disputes ◽  
Classification Prediction ◽  
Deep Learning Model

Most of the preliminary attempts of deep learning in medical images focus on replacing natural images with medical images into convolutional neural networks. In doing so, however, the particularity of medical images and the basic differences between the two types of images are ignored. This difference makes it impossible to directly use the network architecture developed for natural images. This paper therefore uses medical data sets for migration learning. Moreover, the reason why deep learning is difficult to apply in medicine is that it can easily lead to medical disputes because of its unexplainability. In this paper, the deep learning model is explained and implemented by using the theory of fuzzy logic. This paper tests the accuracy and stability of the original model and the new model in classification prediction. Our results show that the model implemented by fuzzy logic improves the accuracy, and makes the prediction more stable as well.

scholarly journals Critical Aspects of Person Counting and Density Estimation

2021 ◽  
Vol 7 (2) ◽  
pp. 21
Author(s):  
Roland Perko ◽  
Manfred Klopschitz ◽  
Alexander Almer ◽  
Peter M. Roth
Keyword(s):  
Density Estimation ◽  
Network Architecture ◽  
Reference Data ◽  
State Of The Art ◽  
Limit State ◽  
Ground Truth ◽  
Data Sets ◽  
Ground Truth Generation ◽  
Baseline Approach ◽  
Critical Aspects

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

scholarly journals A data-driven neural network architecture for sentiment analysis

2019 ◽  
Vol 53 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Erion Çano ◽  
Maurizio Morisio
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Network Models ◽  
Data Sets ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Neural Network Models ◽  
Content Type ◽  
Max Pooling

Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. Design/methodology/approach The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. Findings The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Originality/value Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.

scholarly journals T-SNE visualization of large-scale neural recordings

2016 ◽  
Author(s):  
George Dimitriadis ◽  
Joana Neto ◽  
Adam R. Kampff
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Data Sets ◽  
Neural Recordings ◽  
Sorting Problem ◽  
Feature Spaces ◽  
High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

scholarly journals 1D-CONVOLUTIONAL AUTOENCODER BASED HYPERSPECTRAL DATA COMPRESSION

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 15-21
Author(s):  
J. Kuester ◽  
W. Gross ◽  
W. Middelmann
Keyword(s):  
Dimensional Space ◽  
Hyperspectral Data ◽  
Spectral Dimension ◽  
Sensor Technology ◽  
Spectral Signature ◽  
Support Vector ◽  
Data Sets ◽  
Reconstruction Accuracy ◽  
Spectral Correlation ◽  
Convolutional Autoencoder

Abstract. Hyperspectral sensor technology has been advancing in recent years and become more practical to tackle a variety of applications. The arising issues of data transmission and storage can be addressed with the help of compression. To minimize the loss of important information, high spectral correlation between adjacent bands is exploited. In this paper, we introduce an approach to compress hyperspectral data based on a 1D-Convolutional Autoencoder. Compression is achieved through reducing correlation by transforming the spectral signature into a low-dimensional space, while simultaneously preserving the significant features. The focus lies on compression of the spectral dimension. The spatial dimension is not used in the compression in order not to falsify correlation between the spectral dimension and accuracy of the reconstruction. The proposed 1D-Convolutional Autoencoder efficiently finds and extracts features relevant for compression. Additionally, it can be exploited as a feature extractor or for dimensionality reduction. The hyperspectral data sets Greding Village and Pavia University were used for the training and the evaluation process. The reconstruction accuracy is evaluated using the Signal to Noise Ratio and the Spectral Angle. Additionally, a land cover classification using a multi-class Support Vector Machine is used as a target application. The classification performance of the original and reconstructed data are compared. The reconstruction accuracy of the 1D-Convolutional Autoencoder outperforms the Deep Autoencoder and Nonlinear Principal Component Analysis for the used metrics and for both data sets using a fixed compression ratio.

scholarly journals A Multi-User Schedule Method for Non-Orthogonal Multiple Access in 5G Heterogeneous Network

2019 ◽  
Vol 37 (2) ◽  
pp. 337-343
Author(s):  
Yi Xie
Keyword(s):  
Heterogeneous Network ◽  
Network Architecture ◽  
Multiple Access ◽  
Average Rate ◽  
Small Cells ◽  
Fifth Generation ◽  
Scheduling Method ◽  
New Type ◽  
Multiuser Scheduling ◽  
Better Than

Heterogeneous network is supposed to be the dominant network architecture of the fifth generation (5G) cellular network, which means small cells are overlaid on the macrocell. The beamforming (BF) and cell expansion are two important approaches to serve users in small cells. Furthermore, non-orthogonal multiple access (NOMA) is a new type of multiple access multiplexing which improves system performance without taking up extra spectrum resources. Therefore, it becomes one promising technique in 5G. In this paper, NOMA is applied in a 5G heterogeneous network with biased small cells. The BF strategy and the multiuser scheduling method are proposed. The main user in NOMA is scheduled inside the original coverage of the small cell while the side user is chosen from the biased expansion area. The BF strategy that is executed depends on the channel of main user. The multiuser scheduling method is to maximize the rate performance. The proposed method can provide performance benefits. Simulation results show that the proposed methods can be well applied in heterogeneous networks. The achieved performance gain is approximately twice better than traditional OMA and has 10% improvement to the stochastic schedule method. In addition, the average rate of cell edge users is improved.

scholarly journals Autoencoders for Drug-Target Interaction Prediction

2020 ◽  
Author(s):  
Ming Chen ◽  
Xiuze Zhou
Keyword(s):  
Network Architecture ◽  
Drug Target ◽  
Real Data ◽  
Data Sets ◽  
Nonlinear Method ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Biological Studies ◽  
Latent Features ◽  
Full Interaction

Abstract Background: Because it is so laborious and expensive to experimentally identify Drug-Target Interactions (DTIs), only a few DTIs have been verified. Computational methods are useful for identifying DTIs in biological studies of drug discovery and development. Results: For drug-target interaction prediction, we propose a novel neural network architecture, DAEi, extended from Denoising AutoEncoder (DAE). We assume that a set of verified DTIs is a corrupted version of the full interaction set. We use DAEi to learn latent features from corrupted DTIs to reconstruct the full input. Also, to better predict DTIs, we add some similarities to DAEi and adopt a new nonlinear method for calculation. Similarity information is very effective at improving the prediction of DTIs. Conclusion: Results of the extensive experiments we conducted on four real data sets show that our proposed methods are superior to other baseline approaches.Availability: All codes in this paper are open-sourced, and our projects are available at: https://github.com/XiuzeZhou/DAEi.

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