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A Generative Adversarial Network Based Framework for Specific Emitter Characterization and Identification

A Generative Adversarial Network Based Framework for Specifi...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Jialiang Gong Xiaodong Xu Yufeng Qin Weijie Dong University of Science and Technology of China Hefei China ZTE Corporation Nanjing China

Specific emitter identification (SEI) enables the classification of various unique emitters based on received waveforms using some external feature measurements from their transmit signals and has shown its potential for military and civil applications. However, the characterization of the received waveform is susceptible to various factors in propagation process, resulting in inaccurate representations for the individual emitters, so the discriminative performances of existing methods are usually challenging. To remedy these shortcomings, this paper presents a novel semi-supervised SEI using generative adversarial networks (GAN). We mitigated a representation deep network into Triple-GAN and construct a quadruple-structured framework. the overall feature information hidden in the original signals can be extracted by representation network to improve identification performance while the classification results of Triple-GAN can in turn help the representer learning more discriminative characteristics of individual emitters. Results from both simulations and real world data experiments are provided. Numerical performances indicate our conclusion that our proposed framework constantly outperforms other existing schemes in terms of classification accuracy.

关键词： Gallium nitride Feature extraction Generative adversarial networks Training Knowledge engineering Generators data models

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ChAnot: An intelligent Annotation Tool for Indigenous and Highly Agglutinative Languages in Peru 11

ChAnot: An Intelligent Annotation Tool for Indigenous and Hi...

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11th international conference on Language Resources and Evaluation (LREC)

作者： Mercado-Gonzales, Rodolfo Pereira-Noriega, Jose Sobrevilla, Marco Oncevay, Arturo Pontificia Univ Catolica Peru Dept Ingn Res Grp Artificial Intelligence IA PUCP Lima Peru

ISBN: (纸本)9791095546009

Linguistic corpus annotation is one of the most important phases for addressing Natural Language Processing (NLP) tasks, as these methods are deeply involved with corpus-based techniques. However, meta-data annotation is a highly laborious manual task. A supportive alternative requires the use of computational tools. they are likely to simplify some of these operations, while can be adjusted appropriately to the needs of particular language features at the same time. therefore, this paper presents ChAnot, a web-based annotation tool developed for Peruvian indigenous and highly agglutinative languages, where Shipibo-Konibo was the case study. this new tool is able to support a diverse set of linguistic annotation tasks, such as morphological segmentation markup, POS-tag markup, among others. Also, it includes a suggestion engine based on historic and machine learning models, and a set of statistics about previous annotations.

关键词： Annotation Tool Corpus Annotation Peruvian Indigenous Languages Shipibo-Konibo

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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11th Innovations in Software engineering conference, ISEC 2017

ISBN: (纸本)9781450363983

the proceedings contain 21 papers. the topics discussed include: intelligent software engineering: synergy between AI and software engineering;feature selection techniques to counter class imbalance problem for aging related bug prediction;towards a domain-specific language for the renarration of web pages;COSMIC function points evaluation for software maintenance;an attempt at explicating the relationship between knowledge, systems and engineering;preliminary causal discovery results with software effort estimation data;safety validation using AADL system architecture models;coupling and cohesion metrics for object-oriented software: a systematic mapping study;empirically analyzing and evaluating security features in software requirements;validating requirements reviews by introducing fault-type level granularity: a machine learning approach;a case study on the application of case-based learning in software testing;towards dynamic tenant management for microserve based multi-tenant SaaS applications;relation identification in business rules for domain-specific documents;applying formal methods to design of migrator component of data masking software;state-of-the-art big data security taxonomies;and actor-based methods, concepts and tools for analysing emergent behaviour - an introduction to a model based approach.

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Local Feature Hashing with Graph Regularized Binary Auto-encoder for Face Recognition

Local Feature Hashing with Graph Regularized Binary Auto-enc...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Jing Chen Yunxiao Zu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

In recent years, learning-based hashing has gained increasing research interest in face recognition due to its encouraging storage and computational efficiency. However, learning high-quality binary codes that yield appealing recognition performance is still a challenge. In addition, since the neighborhood structure is important for discrimination, it should be taken into account to capture useful nearest neighbors. In this paper, we propose a novel and efficient graph-based hashing model, called local feature hashing with graph regularized binary auto-encoder (LFH-GRBA), to learn feature representations for face recognition. It considers the semantic neighborhood of the face data and seeks to reconstruct a face image from the learned binary codes. Specifically, the graph Laplacian is incorporated into the binary auto-encoder as a regularizer to exploit the semantical neighborhood information of the face data. To make such a model computational efficiency, a tractable alternating optimization approach is proposed to solve the objective function, yielding high-quality binary codes to well capture the neighborhood structure and provide high discrimination. Moreover, the discrete cyclic coordinate descent (DCC) method is adopted to directly learn binary codes in the Hamming space to eliminate the accumulated quantization errors. Extensive experimental results on three widely used datasets including FERET, LFW and PaSC datasets demonstrate that the proposed LFH-GRBA model outperforms most state-of-the-art face representation methods.

关键词： Face Binary codes Linear programming Optimization Feature extraction Face recognition Image reconstruction

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SharkTank Deal Prediction: dataset and Computational Model

SharkTank Deal Prediction: Dataset and Computational Model

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international conference on Knowledge and Systems engineering (KSE)

作者： thomas Sherk Minh-Triet Tran Tam V. Nguyen Department of Computer Science University of Dayton Dayton United States Department of Computer Science University of Science Ho Chi Minh City Vietnam

SharkTank is a television show where start-ups pitch their idea to a panel of five investors (sharks) in hopes of striking a deal in the form of equity or royalties for money and other business perks. Since its inception, SharkTank has been a center of discussion and analysis for fans, statisticians, and business people alike in hopes of cracking the code to the start-up world and figuring out the formula for the next big `thing'. Most of these discussions and analyses have come in the form of blogs, articles, and academic research. However, there has been a lack of complete datasets and application of computational models for further analysis. In this paper, we investigate factors that play into the SharkTank deal. To this end, we first collect a new dataset, SharkTank Deal dataset (STDD), by combining data from multiple public sources. the dataset includes descriptive features of each start-up such as product category, team composition, valuation, equity offering, specific sharks that appear on that episode, and state origin. For the computational model, we propose a new computational model to predict whether a start-up strikes a deal with a shark. We conduct experiments to demonstrate the superiority of our model over the baselines.

关键词： Predictive models Computational modeling Companies Machine learning Venture capital Investment

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Improved Indoor positioning algorithm using KPCA and ELM

Improved Indoor positioning algorithm using KPCA and ELM

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Lijun Lian Shaobo Xia Sen Zhang Qiong Wu Changqiang Jing Shandong TV University Jinan China University of Science and Technology Beijing Beijing China IoT Engineering School Jiangnan University Wuxi China Linyi University Linyi China

RSS (Received Signal Strength) values which are used in indoor position estimation based on fingerprinting are affected by noise. the RSS value received by the fixed line-of-sight condition points obeys the Gauss distribution and does not match the Gauss distribution. WIFI signal transmission attenuation is also a nonlinear attenuation. this paper presents a joint KPCA-ELM locating algorithm, the use of KPCA (Kernel Principal Component Analysis) of the nonlinear characteristics allow the original RSS data being replaced and dimension reduction, constructing new features. ELM (Extreme learning Machine) is a fast and efficient single-layer feedforward neural network algorithm for training with new characteristics. KPCA-ELM positioning algorithm can effectively reduce the influence of noise on RSS value and improve the accuracy. the experimental results show that KPCA-ELM algorithm can effectively improve the accuracy of indoor positioning.

关键词： Principal component analysis Training Kernel Prediction algorithms Neurons Buildings Wireless fidelity

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A new dimension of breast cancer epigenetics applications of variational autoencoders with DNA methylation 9

A new dimension of breast cancer epigenetics applications of...

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9th international conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Titus, Alexander J. Bobak, Carly A. Christensen, Brock C. Quantitative Biomedical Sciences Dartmouth School of Graduate and Advanced Studies HanoverNH United States Department of Epidemiology Dartmouth Geisel School of Medicine HanoverNH United States Thayer School of Engineering Dartmouth School of Graduate and Advanced Studies HanoverNH United States Department of Molecular and Systems Biology Dartmouth Geisel School of Medicine HanoverNH United States Department of Community and Family Medicine Dartmouth Geisel School of Medicine HanoverNH United States

ISBN: (纸本)9789897582806

In the era of precision medicine and cancer genomics, data are being generated so quickly that it is difficult to fully appreciate the extent of what is discoverable. DNA methylation, a chemical modification to DNA, has been shown to be a significant factor in many cancers and is a candidate data source with ample features for model traing. However, the black-box nature of non-linear models, such as those in deep learning, and a lack of accurately labeled ground truth data have limited the same rapid adoption in this space that other methods have experienced. In this article, we discuss the applications of unsupervised learning through the use of variational autoencoders using DNA methylation data and motivate further work with initial results using breast cancer data provided by the Cancer Genome Atlas. We show that a logistic regression classifier trained on the learned latent methylome accurately classifies disease subtype. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Chemical modification

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Detection and Classification of Cervical Exfoliated Cells Based on Faster R-CNN*

Detection and Classification of Cervical Exfoliated Cells Ba...

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international conference on Advanced Infocomm Technology (ICAIT)

作者： Du Xueyu Li Qinghua Li School of Physics and Electronics Shandong Normal University Jinan China School of Electronic Information Engineering Qilu University of Technology Jinan China

the detection and classification of cervical cells via Pap smear or Liquid-based cytology (LBC) have important clinical significance for pathological diagnosis. For the limitations of pathological images and the complexity of morphological changes of cells, the accurate classification of cervical cells remains a challenging task. DNA ploidy analysis can complete the classification and recognition of individual cells well, and misclassification is common because of the factors of inevitable imaging. Unlike traditional classification methods that rely on manual features, we proposed a region detection and classification method based on multi-semantic label combined with morphological information analysis. We followed the principle that a nucleus represents a cell and divided cells into five categories. the overlapping cell regions could be divided into multiple single cells. In this paper, the data pre-processing was made up for the imbalance in classification. the weight parameters obtained from ImageNet dataset pre-training were realized the transfer learning of cervical exfoliated cells based on Faster R-CNN on LBC dataset. the result showed that the mean average precision (mAP) was 66.98% and the accuracy was 91.61% by learning depth and superficial features of cervical exfoliated cells with ResNet-101 as the backbone. To some extent, it can assist pathologists in the early diagnosis of cervical cancer.

关键词： Image segmentation Feature extraction Tumors Proposals Cervical cancer DNA Pathology

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Computation Offloading and Resource Allocation in Mobile Edge Computing via Reinforcement learning

Computation Offloading and Resource Allocation in Mobile Edg...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Danfeng Wang Jian Zhao School of Electronic Science and Engineering Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

this paper considers the computation offloading and resource allocation for system sum cost minimization in a mobile edge computing (MEC)network. the computation offloading decision of the user equipments (UEs)can be made through wireless link to access the MEC server. Our intent is to minimize the weighted sum cost of the system, which takes into account the energy consumption and time delay, for both task processing and data transmissions, subject to the force delay requirement of the devices. We first formulate the problem as a mixed-integer and non-convex optimization scheme. Enlightened by the superior performance of reinforcement learning (RL)on solving resource control problems, we put forward a novel RL-based scheme for computation offloading and resource allocation in MEC network. Specifically, the MEC node is considered as an “agent”, which first makes decision on whether to offloading the computing task to the MEC server, and then utilizes convex optimization algorithms to settle the radio spectrum and computation resource scheduling problem in each decision phase. the simulation results present that the RL-based method can achieve better computation offloading and resource allocation performance compared to those of local computing and remote computing modes. It also achieves very similar or slightly better performance compared to the genetic algorithm based method.

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data Preprocessing for Intrusion Detection System Using Encoding and Normalization Approaches

Data Preprocessing for Intrusion Detection System Using Enco...

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international conference on Advanced Computing, ICAC

作者： M Srikanth Yadav. R Kalpana. Computer Science and Engineering Pondicherry engineering college Puducherry India

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

this work presents a data preparation and data preprocessing framework to support deep learning and network security experts in producing qualitative data for empirical experimental analysis of intrusion detection data. the Onehotencoder and min-max normalization approaches are used in this proposed preprocessing module. this research paper focuses primarily on analyzing two datasets, specifically KDD Cup' 99, and NSL-KDD datasets, which are commonly used to investigate computer network intrusion detection. the database of the KDD Cup ' 99 consist of five million files, each with 41 attributes that can categorize malicious intrusions into four classes: Probe, DoS, U2R and R2L. the real traffic data cannot be replicated by the KDD cup'99 data set because it was produced over a virtual computer network by simulation. Duplicate and obsolete records were omitted from training and testing in the NSL-KDD database from the KDD Cup ' 99 dataset.

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