DOCSLIB.ORG

RNN with Adaptive Split Algorithm for the Analysis of Security on Cloud Computing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
RNN with Adaptive Split Algorithm for the Analysis of Security on Cloud Computing International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 RNN with Adaptive Split Algorithm for the Analysis of Security on Cloud Computing 1Dr. K. Loheswaran, 2Dr. D. Murali, 3D. Kalaiabirami 1,2Associate Professor, Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad - 501 401. Telangana. India 3Assistant Professor, Department of Computer Science and Engineering, Vivekanandha College of Engineering for Women, Tiruchengode, Tamilnadu, India Abstract One of the most recent trends in IT division is cloud computing (CC). It is an appropriated processing condition which has committed registering assets gotten to whenever from anyplace. In the present time, user keeps a high measure of information on cloud and even share a great deal of information, and thus, it is important to utilize safety efforts so that there is no risk to any of the client's information. To furnish an abnormal state of security with the fast headway of Internet, numerous devices and systems are being utilized. In this study, a Hybrid Adaptive algorithm is developed for the security on Cc. The proposed adaptive machine learning algorithm is the combination of Recurrent Neural Network (RNN) with adaptive split algorithm (ASA). RNN is one of the Artificial Intelligence (AI) techniques, which is utilized to classification purpose. The proposed hybrid adaptive algorithm is utilized to classify the data before the encryption process. Based on the process, the accuracy of the data is achieved and it requires less memory space only. After the data storage allocation, the design of end to end security framework is carried out. The main objective is to secure the data and eliminate the insider threats. The objective of the paper is to increase security by using adaptive split algorithm (ASA) for the transfer of data on cloud servers. The proposed hybrid adaptive method is implemented in JAVA platform and compared with the traditional methods, such as Artificial Neural Network (ANN), Support Vector Machines (SVM),respectively. Moreover, the statistical measures are evaluated Accuracy, Recall, Precision, F- measure, for the proposed and existing methods. Keywords: RNN, Adaptive split Algorithm, security, Cc, SVM and ANN 1. INTRODUCTION IT industries are pushing technology to a whole new level over time. With the elegance of IT, the Internet is one of the most popular technologies these days. It is on the verge of a revolution where resources are globally linked. Therefore, resources can be easily distributed and managed at anytime from anywhere [1]. CC [2] [15] [16] is a fresh computing model that offers organizations a novel commercial classic for adopting IT without prior investment. CC is everywhere now. In most cases, users are using the cloud unknowingly. Small and medium enterprises will move to CC because it supports quick access to their app and reduces the cost of infrastructure. CC is not only a technological solution, it is also a business model that can sell and rent computing power. The definition of CC from the National Institute of Standards and Technology (NIST) is that, on request, CC allows access to a ubiquitous and conveniently efficient network, with a set of configurable computing resources that can be delivered speedily and with least effort. And FIG. Management or service provider. Interference [4] [19]. CC is based on virtualization code, which means that there is only one large machine and more users are sharing this machine for their own dedicated resources. There are three levels of service [5]. One of them is Service Infrastructure (IaaS), which includes hardware tools such as network resources, hard disk, and memory. They are rented and charged according to usage. [17] [18] The second is the Platform as a Service (PaaS), which not only provides all the features as in IAS, but also offers operating system features and updates. [5] [20]. The third is Software as a Service (SaaS), 3191 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 which is the best stretchy and easy to use. It has all the features of IaaS and PaaS, and offers the freedom to select software from a package of resources even now accessible [5] [11]. The main goal of CC is to reduce the computation problem and provide the highest security resources [14]. CC has many benefits, but there are also some challenges, such as security, trust, availability, interoperability and SLA. The user requests to provide their personal identification info, sensitive info, and usage. [6]. Three states that are functionally sensitive, below. ❖ The communication of personal responsive data to the cloud server, ❖ the cloud server communicate to clients computers with data and ❖ The client’s personal data’s are stored in cloud servers, it is remote server not held by the users. The above three CC-sensitive states are particularly vulnerable to security breaches, which conduct research and examination of the security aspects of CC training [7]. Critical requires strong storage, management, sharing and verification of complex data. Establishing patterns and trends to improve the quality of health care is the best way to protect the country and find an alternative. Therefore clouds that need to be fixed are very important [8]. Recently, the network has been integrated with Cloud Machine Learning (ML) for cloud security [9]. In the field of ML, data classification is a method of classifying the classification of unclassified data samples set by the construction classifier [10]. Data mining can be considered as one of the most important things to find knowledge from big data. Different algorithms and techniques are available for data exploitation. Classification techniques can be used in big data to find the mine rule [13]. The propaganda backbone used by ANN is a productive and viable way to guide collaborative learning (CL). The accuracy of back-propagation neural networks depends on the quantity and quality of the data used for the study. CL improves learning accuracy by integrating more number of data sets into the process of learning, compared to learning only with the local data set. CL has become more accessible due to computing architectures such as CC [12]. To incorporate CL over the Internet, it is necessary to provide a solution to allow unreliable parties to conduct common studies on the neural network without exposing their private data sets [12]. This approach is a major challenge and has some drawbacks. Security data sets are very rare, especially due to privacy concerns. Here, machine learning models are trained and evaluated in experimentally generated datasets with insufficient wealth [9]. Therefore, in the proposed technique, we integrated RNA with Adaptive split Algorithm (ASA). RNN is one of the AI techniques used for classification purposes. The proposed hybrid adaptation algorithm is used to classify data before the encryption process. After allowing data storage, the end-to-end security framework will be designed. Therefore, the main purpose of the proposed technology is to reduce data security and penetration. The rest of the papers follow, because section 2 represents a recent literature review, and section 3 defines the proposed methodology. Analysis of the proposed method is displayed in Section 4. Lastly, the concluding section is mentioned in Section 5. 2. Literature Review: A Recent Analysis Lot of research works are presented in the research area of Security on Cc. Some of them are reviewed here, Chiba et.al [21] proposed a cooperative and hybrid network intrusion detection scheme to detect network attacks in the cloud environment by observing network traffic with high service quality and performance. To detect unknown attacks, they have utilized Snort as signature and Back Propagation Neural Network is used when detecting anomaly to reduce the detection time. As the BPN has the disadvantage of slow convergence, optimization algorithm is used to optimize the factors in order to increase the convergence rate. Their proposed technique provides low computational cost, high detection rate and accuracy. 3192 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 Victor and Muthu [22] established a framework known as CC Adoption Framework (CCAF), which was customized to secure cloud data. This report describes the overall image, motivation, and CCAF components for data protection. They used a data center with 10 megabytes of data and imitated the Business process modeling nation (BPMN) to learn how to use the data. The use of the BPMN simulation allowed for the evaluation of selected safety performances before the actual implementation. They demonstrated that multi-layer CCAF security can guard data in real-time and that there are three security layers: 1) firewall and access control; 2) characteristics management and intrusion anticipation and 3) convergent encryption. For CCAF validation, infiltration testing using 10,000 Trojans and viruses involves two groups of ethical hacking experiments. Multi-layer CCAF security able to kill 9,919 viruses and Trojans in a matter of seconds, and the rest can be determined or isolated. Tests have shown that the injection of viruses and Trojans can reduce the percentage of barriers to succession, but 97.43% of them can be shipped. CCAF can be much effective while combined with BPMN model to calculate the security practice and penetrate the test outcomes. For these users, a method proposed by Dong et.al [23] does not reveal the actual model data. Data encryption and Feed-forward propagation are joined in a single process: the first layer of the deep network is migrated to the users' local devices and the nearby workflow, followed by the drop unit's method output method, which can be used to reverse the output. The approach fulfills the privacy requirements of envy and confirms many advantages over the traditional encryption / decryption approach.
Load more

Recommended publications
  • Neural Networks (AI) (WBAI028-05) Lecture Notes
    Herbert Jaeger Neural Networks (AI) (WBAI028-05) Lecture Notes V 1.5, May 16, 2021 (revision of Section 8) BSc program in Artificial Intelligence Rijksuniversiteit Groningen, Bernoulli Institute Contents 1 A very fast rehearsal of machine learning basics 7 1.1 Training data. .............................. 8 1.2 Training objectives. ........................... 9 1.3 The overfitting problem. ........................ 11 1.4 How to tune model flexibility ..................... 17 1.5 How to estimate the risk of a model .................. 21 2 Feedforward networks in machine learning 24 2.1 The Perceptron ............................. 24 2.2 Multi-layer perceptrons ......................... 29 2.3 A glimpse at deep learning ....................... 52 3 A short visit in the wonderland of dynamical systems 56 3.1 What is a “dynamical system”? .................... 58 3.2 The zoo of standard finite-state discrete-time dynamical systems .. 64 3.3 Attractors, Bifurcation, Chaos ..................... 78 3.4 So far, so good ... ............................ 96 4 Recurrent neural networks in deep learning 98 4.1 Supervised training of RNNs in temporal tasks ............ 99 4.2 Backpropagation through time ..................... 107 4.3 LSTM networks ............................. 112 5 Hopfield networks 118 5.1 An energy-based associative memory ................. 121 5.2 HN: formal model ............................ 124 5.3 Geometry of the HN state space .................... 126 5.4 Training a HN .............................. 127 5.5 Limitations ..............................
    [Show full text]
  • Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
    PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction
    [Show full text]
  • Mxnet-R-Reference-Manual.Pdf
    Package ‘mxnet’ June 24, 2020 Type Package Title MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Sys- tems Version 2.0.0 Date 2017-06-27 Author Tianqi Chen, Qiang Kou, Tong He, Anirudh Acharya <https://github.com/anirudhacharya> Maintainer Qiang Kou <[email protected]> Repository apache/incubator-mxnet Description MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity. License Apache License (== 2.0) URL https://github.com/apache/incubator-mxnet/tree/master/R-package BugReports https://github.com/apache/incubator-mxnet/issues Imports methods, Rcpp (>= 0.12.1), DiagrammeR (>= 0.9.0), visNetwork (>= 1.0.3), data.table, jsonlite, magrittr, stringr Suggests testthat, mlbench, knitr, rmarkdown, imager, covr Depends R (>= 3.4.4) LinkingTo Rcpp VignetteBuilder knitr 1 2 R topics documented: RoxygenNote 7.1.0 Encoding UTF-8 R topics documented: arguments . 15 as.array.MXNDArray . 15 as.matrix.MXNDArray . 16 children . 16 ctx.............................................. 16 dim.MXNDArray . 17 graph.viz . 17 im2rec . 18 internals . 19 is.mx.context . 19 is.mx.dataiter . 20 is.mx.ndarray . 20 is.mx.symbol . 21 is.serialized . 21 length.MXNDArray . 21 mx.apply . 22 mx.callback.early.stop . 22 mx.callback.log.speedometer . 23 mx.callback.log.train.metric . 23 mx.callback.save.checkpoint . 24 mx.cpu . 24 mx.ctx.default . 24 mx.exec.backward . 25 mx.exec.forward . 25 mx.exec.update.arg.arrays . 26 mx.exec.update.aux.arrays . 26 mx.exec.update.grad.arrays .
    [Show full text]
  • Jssjournal of Statistical Software MMMMMM YYYY, Volume VV, Issue II
    JSSJournal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/ OSTSC: Over Sampling for Time Series Classification in R Matthew Dixon Diego Klabjan Lan Wei Stuart School of Business, Department of Industrial Department of Computer Science, Illinois Institute of Technology Engineering and Illinois Institute of Technology Management Sciences, Northwestern University Abstract The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the over- sampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to quickly validate the functionality in the package. To demonstrate the performance impact of OSTSC,we then provide two medium size imbalanced time series datasets. Each example applies a TensorFlow implementation of a Long Short-Term Memory (LSTM) classifier - a type of a Recurrent Neural Network (RNN) classifier - to imbalanced time series. The classifier performance is compared with and without oversampling. Finally, larger versions of these two datasets are evaluated to demonstrate the scalability of the package. The examples demonstrate that the OSTSC package improves the performance of RNN classifiers applied to highly imbalanced time series data. In particular, OSTSC is observed to increase the AUC of LSTM from 0.543 to 0.784 on a high frequency trading dataset consisting of 30,000 time series observations. arXiv:1711.09545v1 [stat.CO] 27 Nov 2017 Keywords: Time Series Oversampling, Classification, Outlier Detection, R. 1. Introduction A significant number of learning problems involve the accurate classification of rare events or outliers from time series data.
    [Show full text]
  • Verification of RNN-Based Neural Agent-Environment Systems
    The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) Verification of RNN-Based Neural Agent-Environment Systems Michael E. Akintunde, Andreea Kevorchian, Alessio Lomuscio, Edoardo Pirovano Department of Computing, Imperial College London, UK Abstract and realised via a previously trained recurrent neural net- work (Hausknecht and Stone 2015). As we discuss below, We introduce agent-environment systems where the agent we are not aware of other methods in the literature address- is stateful and executing a ReLU recurrent neural network. ing the formal verification of systems based on recurrent We define and study their verification problem by providing networks. The present contribution focuses on reachability equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete analysis, that is we intend to give formal guarantees as to procedure for their verification against properties specified whether a state, or a set of states are reached in the system. in a simplified version of LTL on bounded executions. We Typically this is an unwanted state, or a bug, that we intend present an implementation and discuss the experimental re- to ensure is not reached in any possible execution. Similarly, sults obtained. to provide safety guarantees, this analysis can be used to show the system does not enter an unsafe region of the state space. 1 Introduction The rest of the paper is organised as follows. After the A key obstacle in the deployment of autonomous systems is discussion of related work below, in Section 2, we introduce the inherent difficulty of their verification. This is because the basic concepts related to neural networks that are used the behaviour of autonomous systems is hard to predict; in throughout the paper.
    [Show full text]
  • Anurag Soni's Blog
    ANURAG SONI 447 Sharon Rd Pittsburgh, PA, USA |[email protected] | 765-775-0116 | GitHub | Blog | LinkedIn PROFILE • MS Business Analytics student seeking position in data science and decision-making business units • 5 years of experience as Analyst/Product Mgr. for a technology consulting firm with Investment Banking clients • Skills in figuring out impactful stories with data EDUCATION Purdue University, Krannert School of Management West Lafayette, IN Master of Science in Business Analytics and Information Management May 2018 Indian Institute of Technology Guwahati Guwahati, INDIA Bachelor of Technology, Chemical Engineering May 2011 HIGHLIGHTS • Tools: PL/SQL, Python, R, Excel(VBA), SAS, MATLAB, Tableau, Gurobi • Environment: TensorFlow, Keras, Hadoop, Hive, Docker, Google Cloud, UNIX • Process: Data Mining, SDLC, Process Simulation, Optimization, Data Modeling • Skills: Client-Facing, Strong Communication, Change Management, Team Management, Mentoring EXPERIENCE Purdue BIZ Analytics LAB West Lafayette, IN Associate Analyst(Co-Op) July 2017 – Mar 2018 • Demand Forecasting of supermarket sales: Built a production ready machine-learning solution using - Python deep learning libraries with results expecting to save significant inventory costs • Delivery Rebate Forecast for retailers: Built a forecasting model that supports reporting of weekly expected rebate numbers in an annual tiered delivery contract. • Decision Support System for Music Producer: Implemented dashboard application that uses Spotify datasets to partially cluster songs by their popularity and revenue capability • Production implementation of ML solution: Created an automated machine learning pipeline of an object- detection solution built using TensorFlow on Google Cloud using Docker and Kubernetes technology • Hate Speech Recognition using NLP: Implemented a text mining model that identifies hate speech characteristics in the text.
    [Show full text]
  • Deep Learning Architectures for Sequence Processing
    Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER Deep Learning Architectures 9 for Sequence Processing Time will explain. Jane Austen, Persuasion Language is an inherently temporal phenomenon. Spoken language is a sequence of acoustic events over time, and we comprehend and produce both spoken and written language as a continuous input stream. The temporal nature of language is reflected in the metaphors we use; we talk of the flow of conversations, news feeds, and twitter streams, all of which emphasize that language is a sequence that unfolds in time. This temporal nature is reflected in some of the algorithms we use to process lan- guage. For example, the Viterbi algorithm applied to HMM part-of-speech tagging, proceeds through the input a word at a time, carrying forward information gleaned along the way. Yet other machine learning approaches, like those we’ve studied for sentiment analysis or other text classification tasks don’t have this temporal nature – they assume simultaneous access to all aspects of their input. The feedforward networks of Chapter 7 also assumed simultaneous access, al- though they also had a simple model for time. Recall that we applied feedforward networks to language modeling by having them look only at a fixed-size window of words, and then sliding this window over the input, making independent predictions along the way. Fig. 9.1, reproduced from Chapter 7, shows a neural language model with window size 3 predicting what word follows the input for all the. Subsequent words are predicted by sliding the window forward a word at a time.
    [Show full text]
  • Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
    Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges TRIET H. M. LE, The University of Adelaide HAO CHEN, The University of Adelaide MUHAMMAD ALI BABAR, The University of Adelaide Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Neural networks; Natural language processing; • Software and its engineering → Software notations and tools; Source code generation; Additional Key Words and Phrases: Deep learning, Big Code, Source code modeling, Source code generation 1 INTRODUCTION Deep Learning (DL) has recently emerged as an important branch of Machine Learning (ML) because of its incredible performance in Computer Vision and Natural Language Processing (NLP) [75]. In the field of NLP, it has been shown that DL models can greatly improve the performance of many classic NLP tasks such as semantic role labeling [96], named entity recognition [209], machine translation [256], and question answering [174].
    [Show full text]
  • Julia: Next Generation Language Scipy 2019, Universidad De Los Andes
    Julia: Next Generation Language SciPy 2019, Universidad de los Andes Let us understand the necessity: Scientific AI Machine Learning + Domain Power modeling AI & Robotics Team Kiwibot Do I have to learn another language? Current situation: I'm here talking to experts in the scientific python community At the moment we have the two language problem in Scientific AI When I was TA in tools for computing: ● NumPy for numerical linear algebra. ● R for statistical analysis. ● C for the fast stuff ● Bash to tie all up A.k.a "Ousterholt's Dichotomy" "System languages" "Scripting languages" ● Static ● Dynamic ● Compiled ● Interpreted ● User types ● Standard types AI & Robotics Team Kiwibot The Two Language Problem ? Because of this dichotomy, a two-tier compromise is standard: ● For convenience, use a a scripting language (Matlab, R, Python) ● But do all the hard stuff in a system language (C, C++, Fortran) Pragmatic for many applications, but has drawbacks ● Aren't the hard parts exactly where you need an easier language? ● Forces vectorization everywhere, even when awkward or wasterful ● Creates a social barrier - a wall between users and developers @ucb_notanartist Enter The Julian Unification ● Dynamic ● Compiled ● User Types and standard types ● Standalone or glue AI & Robotics Team Kiwibot Julia is Fast!! Let us understand the necessity: Scientific AI Machine Learning + Domain Power modeling AI + Robotics Team Kiwibot Mechanistics vs Non-Mechanistics Models Let's say we want to define a nonlinear model for the population of rabbits. There are three basic ways to do this: Rabbits tomorrow = Model(Rabbits today) ● Analytical solutions require that someone explicitly define a nonlinear model.
    [Show full text]
  • Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
    water Article Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting Halit Apaydin 1 , Hajar Feizi 2 , Mohammad Taghi Sattari 1,2,* , Muslume Sevba Colak 1 , Shahaboddin Shamshirband 3,4,* and Kwok-Wing Chau 5 1 Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey; [email protected] (H.A.); [email protected] (M.S.C.) 2 Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz 51666, Iran; [email protected] 3 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam 4 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam 5 Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China; [email protected] * Correspondence: [email protected] or [email protected] (M.T.S.); [email protected] (S.S.) Received: 1 April 2020; Accepted: 21 May 2020; Published: 24 May 2020 Abstract: Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language.
    [Show full text]
  • Neural Network Architectures and Activation Functions: a Gaussian Process Approach
    Fakultät für Informatik Neural Network Architectures and Activation Functions: A Gaussian Process Approach Sebastian Urban Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Prof. Dr. rer. nat. Stephan Günnemann Prüfende der Dissertation: 1. Prof. Dr. Patrick van der Smagt 2. Prof. Dr. rer. nat. Daniel Cremers 3. Prof. Dr. Bernd Bischl, Ludwig-Maximilians-Universität München Die Dissertation wurde am 22.11.2017 bei der Technischen Universtät München eingereicht und durch die Fakultät für Informatik am 14.05.2018 angenommen. Neural Network Architectures and Activation Functions: A Gaussian Process Approach Sebastian Urban Technical University Munich 2017 ii Abstract The success of applying neural networks crucially depends on the network architecture being appropriate for the task. Determining the right architecture is a computationally intensive process, requiring many trials with different candidate architectures. We show that the neural activation function, if allowed to individually change for each neuron, can implicitly control many aspects of the network architecture, such as effective number of layers, effective number of neurons in a layer, skip connections and whether a neuron is additive or multiplicative. Motivated by this observation we propose stochastic, non-parametric activation functions that are fully learnable and individual to each neuron. Complexity and the risk of overfitting are controlled by placing a Gaussian process prior over these functions. The result is the Gaussian process neuron, a probabilistic unit that can be used as the basic building block for probabilistic graphical models that resemble the structure of neural networks.
    [Show full text]
  • HGC: a Fast Hierarchical Graph-Based Clustering Method
    Package ‘HGC’ September 27, 2021 Type Package Title A fast hierarchical graph-based clustering method Version 1.1.3 Description HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. License GPL-3 Encoding UTF-8 SystemRequirements C++11 Depends R (>= 4.1.0) Imports Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo Rcpp, RcppEigen Suggests BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) VignetteBuilder knitr biocViews SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Config/testthat/edition 3 NeedsCompilation yes git_url https://git.bioconductor.org/packages/HGC git_branch master git_last_commit 61622e7 git_last_commit_date 2021-07-06 Date/Publication 2021-09-27 1 2 CKNN.Construction Author Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer XGlab <[email protected]> R topics documented: CKNN.Construction . .2 FindClusteringTree . .3 HGC.dendrogram . .4 HGC.parameter . .5 HGC.PlotARIs . .6 HGC.PlotDendrogram . .7 HGC.PlotParameter . .8 KNN.Construction . .9 MST.Construction . 10 PMST.Construction . 10 Pollen . 11 RNN.Construction . 12 SNN.Construction . 12 Index 14 CKNN.Construction Building Unweighted Continuous K Nearest Neighbor Graph Description This function builds a Continuous K Nearest Neighbor (CKNN) graph in the input feature space using Euclidean distance metric.
    [Show full text]