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Learned Turbo-type Affine Rank Minimization

Learned Turbo-type Affine Rank Minimization

引用

international conference on Wireless Communications and Signal Processing (WCSP)

作者： Xuehai He Xiaojun Yuan Zhipeng Xue Center for Intelligent Networking and Communications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China School of Information Science and Technology ShanghaiTech University Shanghai China

this paper is focused on the efficient algorithm design for affine rank minimization (ARM). Given the proliferation of the literature on the ARM problem, the existing algorithms mostly take a model-based approach in the algorithm design, and so are sensitive to the generation model of the linear measurement operator or to the generation model of the low-rank matrix to be recovered. It is known that, all these algorithms do not work well, e.g., when the low-rank matrix is ill-conditioned. As inspired by the success of learned iterative soft-thresholding (LIST) and learned approximate message passing (LAMP), we develop a learning-based message passing algorithm named learned turbo-type affine rank minimization (LTARM) to cope with the ARM problem. the LTARM algorithm learns its parameters from data, and hence is robust to the generation models of the linear operator and the low-rank matrix. We derive analytical expressions for the partial derivatives involved in training the LTARM network. Given the large size of the low-rank matrix in a typical ARM problem, these analytical expressions are of essential importance for the development of computationally feasible training in LTARM networks. Numerical results demonstrate that LTARM significantly outperforms the state-of-the-art counterparts for various generation models of the linear operator and the low-rank matrix, especially when the low-rank matrix is ill-conditioned.

关键词： Minimization Approximation algorithms Training Message passing Signal processing algorithms Neural networks Iterative algorithms

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Exploring Online Novelty Detection Using First Story Detection Models 1

引用

19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Wang, Fei Ross, Robert J. Kelleher, John D. Dublin Inst Technol Sch Comp Dublin Ireland ADAPT Res Ctr Dublin Ireland

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the "Beast from the East" hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a clear definition of novelty. Indeed, we argue the definition of novelty is the key issue in designing a good detection model. Within the context of FSD, we first categorise online novelty detection models into three main categories, based on different definitions of novelty scores, and then compare the performances of these model categories in different features spaces. Our experimental results show that the challenge of FSD varies across novelty scores (and corresponding model categories);and, furthermore, that the detection of novelty in the very popular Word2Vec feature space is more difficult than in a normal frequency-based feature space because of a loss of word specificity.

关键词： Online novelty detection First Story Detection (FSD) Unsupervised learning Novelty score Feature space Word2Vec

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Real-time low SNR signal processing for nanoparticle analysis with deep neural networks 11

Real-time low SNR signal processing for nanoparticle analysi...

引用

11th international conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Lenssen, Jan Eric Toma, Anas Seebold, Albert Shpacovitch, Victoria Libuschewski, Pascal Weichert, Frank Chen, Jian-Jia Hergenröder, Roland CS VII - Computer Graphics TU Dortmund University Otto-Hahn-Straße 16 Dortmund44227 Germany CS XII - Embedded Systems TU Dortmund University Otto-Hahn-Straße 16 Dortmund44227 Germany Leibniz-Institute for Analytical Science ISAS e.V. Bunsen-Kirchhoff-Straße 11 Dortmund44139 Germany

ISBN: (纸本)9789897582790

In this work, we improve several steps of our PLASMON ASSISTED MICROSCOPY OF NANO-SIZED OBJECTS (PAMONO) sensor data processing pipeline through application of deep neural networks. the PAMONO-biosensor is a mobile nanoparticle sensor utilizing SURFACE PLASMON RESONANCE (SPR) imaging for quantification and analysis of nanoparticles in liquid or air samples. Characteristics of PAMONO sensor data are spatiotemporal blob-like structures with very low SIGNAL-TO-NOISE RATIO (SNR), which indicate particle bindings and can be automatically analyzed with image processing methods. We propose and evaluate deep neural network architectures for spatiotemporal detection, time-series analysis and classification. We compare them to traditional methods like frequency domain or polygon shape features classified by a Random Forest classifier. It is shown that the application of deep learning enables our data processing pipeline to automatically detect and quantify 80 nm polystyrene particles and pushes the limits in blob detection with very low SNRs below one. In addition, we present benchmarks and show that real-time processing is achievable on consumer level desktop GRAPHICS PROCESSING UNITs (GPUs). Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Signal to noise ratio

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Predicting Solar Performance Ratio Based on Encoder-Decoder Neural Network Model

Predicting Solar Performance Ratio Based on Encoder-Decoder ...

引用

international conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： Chih-Feng Yen He-Yen Hsieh Kuan-Wu Su Jenq-Shiou Leu IEEE Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fault diagnosis of photovoltaic (PV) arrays is an important task for improving the reliability and safety of the overall photovoltaic system. In the traditional fault diagnosis, most of the past researches focus on the I-V curve to determine the PV arrays state. However, most inverters as smart meters cannot obtain data in high frequency and high volume in order to calculate the I-V curve, and the information usually cannot be retrieved conveniently. In this paper, we evaluate various Recurrent Neural Network Models' from the recent advances in the deep learning field and their abilities in predicting PV arrays power generation. the results show the Encoder-Decoder model using Convolutional Neural Network (CNN) as the decoder and Temporal Convolutional Network (TCN) are more effective in predicting the PV arrays solar output production and can be used as the foundation for fault diagnosis and prediction.

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Developing Professional Skills through Facilitated Dialogue 11th

Developing Professional Skills Through Facilitated Dialogue

引用

11th international conference on Interactive Mobile Communication Technologies and learning (IMCL)

作者： Juvonen, Pasi Kurvinen, Anu Saimaa Univ Appl Sci Fac Business Adm Lappeenranta Finland

ISBN: (纸本)9783319751757;9783319751740

this article is a part of developing Experimental Development Ecosystem (EDE). EDE is a framework including researchers from University of Applied Sciences and a Technological University, students from both institutes, team entrepreneurs and lecturers, local enterprises and local cities. Learner-centric methods utilized with team entrepreneurs and practioners have now been tested in other contexts as well. the article summarizes the experiences of piloting method of professional dialogue with (1) study course for professional MBA students in the international study programme (2) research project, and (3) long term coaching process. In the study, survey data and field notes from observations of dialogues are used as main data sources. Ongoing discussions and feedback from stakeholder groups of the EDE have served as a secondary source of data. the data was analyzed with qualitative data analysis methods. Grounded theory is used as method for data analysis. the framework for developing the EDE has been action research. the data shows that students are usually both motivated and excited about professional dialogue as a learning method. It was also revealed that making oneself familiar with professional dialogue requires repetition and reflection of what has been learned. the reflection needs to cover both content and process to be successful. As a consequence, this the method is also showing good results in learning as the students who graduate are having excellent competences for working life. Local company representatives have also expressed their satisfaction to knowledge, skills, and character of the graduates. therefore, the professional dialogue is currently spread to other student groups studying bachelor and master studies.

关键词： Professional dialogue Collaborative learning Experimental Development Ecosystem 21st century education requirements learning

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the development of learning material using learning cycle 5E model based stem to improve students' learning outcomes in thermochemistry 1

The development of learning material using learning cycle 5E...

引用

international conference on Science Education (ICoSEd)

作者： Sugiarti, A. C. Suyatno, S. Sanjaya, I. G. M. Univ Negeri Surabaya Post Grad Program Sci Educ Surabaya Indonesia

the objective of this study is describing the feasibility of Learnig Cycle 5E STEM (Science, Technology, engineering, and Mathematics) based learning material which is appropriate to improve students' learning achievement in thermochemistry. the study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents' learning outcomes. the subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. the techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. the conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students' learning outcomes in studying thermochemistry.

关键词： Students

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Behavioral analysis at scale: learning course prerequisite structures from learner clickstreams 11

Behavioral analysis at scale: Learning course prerequisite s...

引用

11th international conference on Educational data Mining, EDM 2018

作者： Chen, Weiyu Lan, Andrew S. Cao, Da Brinton, Christopher Chiang, Mung Advanced Research Zoomi Inc. United States Department of Electrical Engineering Princeton University United States College of Engineering Purdue University United States

Knowledge of prerequisite dependencies is crucial to several aspects of learning, from the organization of learning content to the selection of personalized remediation or enrichment for each learner. As the amount of content is scaled up, however, it becomes increasingly difficult to manually specify all of the prerequisites among the different content parts, necessitating automation. Since existing approaches to automatically inferring prerequisite dependencies rely on analysis of content (e.g., topic modeling of text) or performance (e.g., quiz results tied to content) data, they are not feasible in cases where courses have no assessments or only short content pieces (e.g., short video segments). In this paper, we propose an algorithm that extracts prerequisite information using learner behavioral data instead of content and performance data, and apply it to an online short course. By modeling learner interaction with course content through a recurrent neural network-based architecture, our algorithm characterizes the prerequisite structure as latent variables, and estimates them from learner behavior. through evaluation on a dataset of roughly 12,000 learners in a course we hosted on our platform, we show that our algorithm excels at both predicting behavior and revealing fine-granular insights into prerequisite dependencies between content segments, with validation provided by a course administrator. Our approach of content analytics using large-scale behavioral data complements existing approaches that focus on course content and/or performance data. © 2018 international Educational data Mining Society. All rights reserved.

关键词： Recurrent neural networks

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2018 IEEE international conference on Evolving and Adaptive intelligent Systems, EAIS 2018

2018 IEEE International Conference on Evolving and Adaptive ...

引用

11th IEEE international conference on Evolving and Adaptive intelligent Systems, EAIS 2018

ISBN: (纸本)9781538613764

the proceedings contain 20 papers. the topics discussed include: soft computing forecasting of cardiovascular and respiratory incidents based on climate change scenarios;uninorm based regularized fuzzy neural networks;EDUC8: self-evolving and personalized learning pathways utilizing semantics;deep reinforcement learning for frontal view person shooting using drones;active fuzzy rule induction;incremental adaptive semi-supervised fuzzy clustering for data stream classification;interval-valued intuitionistic fuzzy cognitive maps for stock index forecasting;constructing fuzzy numbers from arbitrary statistical intervals;color adaptation for protanopia using differential evolution-based fuzzy clustering: a case study in digitized paintings;an adaptable deep learning system for optical character verification in retail food packaging;an incremental self-trained ensemble algorithm;scheduling the execution of tasks at the edge;predicting changes in quality of life for patients in vocational rehabilitation;real-time video thresholding using evolutionary techniques and cross entropy;and multi-objective optimization of charging infrastructure to improve suitability of commercial drivers for electric vehicles using real travel data.

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On the Comparison: Random Forest, SMOTE-Bagging, and Bernoulli Mixture to Classify Bidikmisi dataset in East Java

On The Comparison: Random Forest, SMOTE-Bagging, and Bernoul...

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Joint international conference on Computer engineering, Network and intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional conference on Computer and Information engineering (RCCIE)

作者： Iriawan, Nur Fithriasari, Kartika Ulama, Brodjol Sutijo Suprih Suryaningtyas, Wahyuni Pangastuti, Sinta Septi Cahyani, Nita Qadrini, Laila Inst Teknol Sepuluh Nopember Fac Math Comp & Data Sci Dept Stat Surabaya Indonesia

ISBN: (纸本)9781538675090

the Bidikmisi is a scholarship program from the Indonesian government that intended for students who are not economically capable, but they have good academic performance. In the implementation of the Bidikmisi scholarship program, there are indications of a problem, namely the condition of inaccurate allocation in the Bidikmisi scholarship that is accepted or unaccepted. the purpose of this study was to examine several comparison methods that were used to get the accuracy allocation of the Bidikmisi scholarship in East Java. these methods include random forest, SMOTE-Bagging, and Bernoulli mixture model. Based on the AUC and g-mean values, the Bernoulli mixture method has a better proficiency than the random forest and SMOTE-Bagging.

关键词： AUC bagging Bernoulli mixture g-mean random forest SMOTE

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intelligent Monitoring of Vital Parameters in Wireless Applications: Android Solution for Pulse and Body Temperature

Intelligent Monitoring of Vital Parameters in Wireless Appli...

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Radu Rădescu Rareş Niculescu Applied Electronics and Information Engineering University “Politehnica” of Bucharest Bucharest Romania

ISBN: (数字)9781728116242

ISBN: (纸本)9781728116259

In this paper we have set up a system to monitor certain vital parameters such as pulse and body temperature. the application thus developed displays the recorded values and measured sensor data is transmitted through wireless technologies to a mobile computing unit. the application uses a variety of IT systems, both hardware and software. In the first part of the paper is presented the current study of wireless communications. the standard used in the paper is 802.11 (via Wi-Fi). A study on the use of databases using MySQL is also presented. the Raspberry Pi 3 Model B, as a local server, is used to store the data (registered parameters of the sensors). the last part of the paper describes how to send data to an application developed for Android. An intelligent system has been implemented at the application level to allow detection of certain irregularities. these observations are made by analyzing recorded parameters. All information is displayed in a graphical interface. As this application has as main purpose the realization of a complex system that combines various methodologies and solutions, a comparative study of the different current technologies has been carried out in order to highlight the performances and the possibilities for improvement.

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