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Connectivity-Optimized Representation learning via Persistent Homology 36

Connectivity-Optimized Representation Learning via Persisten...

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36th international conference on machine learning (ICML)

作者： Hofer, Christoph D. Kwitt, Roland Dixit, Mandar Niethammer, Marc Salzburg Univ Dept Comp Sci Salzburg Austria Microsoft Redmond WA USA UNC Chapel Hill NC USA

ISBN: (纸本)9781510886988

We study the problem of learning representations with controllable connectivity properties. This is beneficial in situations when the imposed structure can be leveraged upstream. In particular, we control the connectivity of an autoencoder's latent space via a novel type of loss, operating on information from persistent homology. Under mild conditions, this loss is differentiable and we present a theoretical analysis of the properties induced by the loss. We choose one-class learning as our upstream task and demonstrate that the imposed structure enables informed parameter selection for modeling the in-class distribution via kernel density estimators. Evaluated on computer vision data, these one-class models exhibit competitive performance and, in a low sample size regime, outperform other methods by a large margin. Notably, our results indicate that a single autoencoder, trained on auxiliary (unlabeled) data, yields a mapping into latent space that can be reused across datasets for one-class learning.

关键词： learning systems

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Electrical Energy Consumption Prediction Using machine learning 1

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11th international conference on ICT Innovations (ICT Innovations)

作者： Stankoski, Simon Kiprijanovska, Ivana Ilievski, Igor Slobodan, Jovanovski Gjoreski, Hristijan Univ Ss Cyril & Methodius Skopje Fac Elect Engn & Informat Technol Skopje North Macedonia ITS Iskratel Skopje North Macedonia

ISBN: (数字)9783030331108

ISBN: (纸本)9783030331108;9783030331092

The paper presents a machine learning (ML) approach to household Electrical Energy (EE) consumption prediction. It includes: data preprocessing, feature engineering, learning a classification model, and experimental evaluation on one of the largest datasets for household EE consumption - dataPort dataset. Beside the features extracted on the historical EE consumption, we additionally analyze weather and contextual-calendar related features. We believe that the combination of multiple sources of data (calendar, weather, historical EE consumption) provides more information to the model in order to learn better performing model. The experimental results showed that in all the cases the ML algorithms outperform the baselines, with the best performing the XGBoost - achieved 0.69 RMSE score, 0.41 MAE score and 0.67 R-2 score which is significantly better than the best performing baseline model (the value from 24 h ago). Additionally, the results show that the largest errors are made for the weekends, which was expected due to the irregularities in the schedule - trips, vacations, etc.

关键词： Energy consumption Prediction Forecast machine learning Deep learning Household Day-ahead

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learning COMPACT PARTIAL DIFFERENTIAL EQUATIONS FOR COLOR IMAGES WITH EFFICIENCY 44

LEARNING COMPACT PARTIAL DIFFERENTIAL EQUATIONS FOR COLOR IM...

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44th IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhao, Zhenyu Hou, Chenping Lin, Bo Fang, Cong Natl Univ Def Technol Coll Liberal Arts & Sci Changsha Hunan Peoples R China Peking Univ Sch EECS Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9781479981311

learning Partial Differential Equations (LPDEs) from training data for particular tasks has been successfully applied to many image processing problems. In this paper, we aim to learn compact Partial Differential Equations (LCPDEs) for color image tasks by proposing a more effective algorithm. The LCPDEs system is formulated as a linear combination of fundamental differential invariants and simplified by omitting the PDE which works as an indicate function. We replace L2-norm with L1-norm to regularize the coefficients with respect to the invariants. As the objective function is non-smooth, we resort to proximal algorithm to optimize it, which ensures convergence in an at least sub-linear rate. Experiments demonstrate the advantages of the proposed method over other PDE-based methods in terms of both quality and efficiency.

关键词： Partial differential equations L1-norm regularization Proximal algorithm Color images

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Can EEG signal predict learners’ perceived difficulty? 27

Can EEG signal predict learners’ perceived difficulty?

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27th international conference on Computers in Education, ICCE 2019

作者： Kumar, Aman Chavan, Pankaj Mitra, Ritayan Department of Computer science and Engineering Thapar Institute of Engineering and Technology India IDP in Educational Technology Indian Institute of Technology Bombay India

ISBN: (纸本)9789869721431

This study presents an approach to predict learner’s perceived difficulty using features extracted from electroencephalography (EEG) data. We demonstrate how EEG signals can be used effectively to estimate learner’s perceived difficulty of learning content. Student self-reports of perceived difficulty and EEG data were gathered from 9 participants who watched a video lecture. A machine learning model with random forest classifier achieved a maximum accuracy of 75.24% in estimating perceived difficulty. Furthermore, the model predicted the difficulty level of the entire video lecture for individuals fairly well. Our results have implications for intelligent tutoring systems which aim at providing the learner with an adaptive and personalized learning environment. © 2019 international conference on Computers in Education, *** right reserved.

关键词： Electroencephalography

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An OPC UA machine learning Server for Automated Guided Vehicle 11th

An OPC UA Machine Learning Server for Automated Guided Vehic...

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11th international conference on Computational Collective Intelligence (ICCCI)

作者： Cupek, Rafal Golczynski, Lukasz Ziebinski, Adam Silesian Tech Univ Inst Informat Gliwice Poland AIUT Sp Zoo Ltd Gliwice Poland

ISBN: (纸本)9783030283742;9783030283735

Preparing training data sets for supervised machine learning is particularly difficult when the input information has a serial nature and the data sources are non-deterministic. This paper discusses a problem related to preparing a data set for the machine learning algorithms that are used for Automated Guided Vehicles (AGV). An OPC UA server that is dedicated for machine learning support was designed in order to comply with the communication standards and information models that are used in the new generation of manufacturing systems. The proposed approach not only utilises communication features of OPC UA technology but also its rich possibilities for information modelling. The OPC UA server that was created converts raw input data into a format that can be easily applied for the machine learning process. The presented solution is dedicated for the AGV that are used for internal logistics in flexible production systems. The authors discuss the different information models that are available for the OPC UA standard and explain the design choices that were made when preparing the server. The presented solution was verified during the development process for a new family of AGV that is being designed and produced by the AIUT Company.

关键词： Automated Guided Vehicle AGV machine learning Communication middleware OPC UA (IEC 62541) machine-to-machine (M2M) communication

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Automated Classification of Web-Application Attacks for Intrusion Detection 1

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9th international conference on Security, Privacy, and applied Cryptography Engineering (SPACE)

作者： Bhagwani, Harsh Negi, Rohit Dutta, Aneet Kumar Handa, Anand Kumar, Nitesh Shukla, Sandeep Kumar Indian Inst Technol C3I Ctr Dept CSE Kanpur Uttar Pradesh India

ISBN: (数字)9783030358693

ISBN: (纸本)9783030358693;9783030358686

In today's information driven society and economy, web facing applications are most common way to run information dissemination, banking, e-commerce etc. Web applications are frequently targeted by attackers through intelligently crafted http requests to exploit vulnerabilities existing in the application, front-end, and the web-clients. Some of the most frequent such attacks are SQL Injection, Cross-Site Scripting, Path-traversal, Command Injection, Cross-site request forgery etc. Detecting these attacks up front and blocking them, or redirecting the request to a honey-pot could be a way to prevent web applications from being exploited. In this work, we developed a number of machine learning models for detecting and classifying http requests into normal, and various types of attacks. Currently, the models are applied as an ensemble on the http server logs, to classify and build data analytics on the http requests received by any web server in order to garner threat intelligence, and threat landscape. We also implemented an online log-analysis version that analyzes logs every 15 s to classify http requests in the recent 15 s. However, it can also be used as a web application firewall to block the http requests based on the classification results. We also have implemented an intrusion protection mechanism by redirecting http requests classified upfront as malicious towards a web honeypot. We compare various existing signature based, regular expression based, and machine learning based techniques against our models for detection and classification of http based attacks, and show that our methods achieve better performance over existing techniques.

关键词： Intrusion detection system Web security machine learning

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A Crystal/Clear Pipeline for applied Image Processing 1

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5th Asian conference on Supercomputing Frontiers (SCFA)

作者： Watkins, Christopher J. Rosa, Nicholas Carroll, Thomas Ratcliffe, David Ristic, Marko Russell, Christopher Li, Rongxin Fazio, Vincent Newman, Janet CSIRO Sci Comp Clayton Vic 3181 Australia CSIRO Mfg Clayton Vic 3181 Australia Univ Melbourne Parkville Vic 3052 Australia CSIRO Data61 Acton ACT 2601 Australia CSIRO Sci Comp Alexandria NSW 1435 Australia CSIRO Data61 Marsfield NSW 2122 Australia CSIRO Minerals Clayton Vic 3181 Australia

ISBN: (数字)9783030186456

ISBN: (纸本)9783030186456;9783030186449

Many long-standing image processing problems in applied science domains are finding solutions through the application of deep learning approaches to image processing. Here we present one such application;the case of classifying images of protein crystallisation droplets. The Collaborative Crystallisation Centre in Melbourne, Australia is a medium throughput service facility that produces between five and twenty thousand images per day. This submission outlines a reliable and robust machine learning pipeline that autonomously classifies these images using CSIRO's high-performance computing facilities. Our pipeline achieves improved accuracies over existing implementations and delivers these results in real time. We discuss the specific tools and techniques used to construct the pipeline, as well as the methodologies for testing and validating externally developed classification models.

关键词： machine learning Deep learning Crystallisation image Image processing Automation Classification

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Automatic Essay Grading for Bahasa Indonesia with Support Vector machine and Latent Semantic Analysis 3

Automatic Essay Grading for Bahasa Indonesia with Support Ve...

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3rd international conference on Electrical Engineering and Computer science (ICECOS)

作者： Ratna, Anak Agung Putri Khairunissa, Hanifah Kaltsum, Aaliyah Ibrahim, Ihsan Purnamasari, Prima Dewi Univ Indonesia Fac Engn Dept Elect Engn Depok Indonesia

ISBN: (纸本)9781728147147

This research is used to increase the accuracy of automatic short essay grading for Bahasa Indonesia. Short essay in Bahasa Indonesia are classified using Support Vector machine (SVM) based on its topic to decrease unrelated answer then assessed using Latent Semantic Analysis. Term Frequency-Inverse Document Frequency (TF-IDF) is used to weigh word in short essay and the result will be an input on SVM. The output of SVM are assessed using Latent Semantic Analysis (LSA). Latent Semantic Analysis uses Term Frequency Matrix to represent text in matrix, Singular Value Decomposition to decompose these matrix, and Frobenius Norm to find the similarity of lectures' answer and students' answer In this research, parameter C value as 1 and kernel linear are used to obtain the highest accuracy of classification using Support Vector machine, 97,297% with 50% portion of data as training and 50% portion of data as testing. The accuracy score obtained from LSA is 72,01%.

关键词： essay grading e-learning Japanese language latent semantic analysis term frequency-inverse document frequency support vector machine

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Transfer learning for Information Extraction with Limited data 16th

Transfer Learning for Information Extraction with Limited Da...

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16th international conference of the Pacific Association for Computational Linguistics, PACLING 2019

作者： Nguyen, Minh-Tien Phan, Viet-Anh Linh, Le Thai Son, Nguyen Hong Dung, Le Tien Hirano, Miku Hotta, Hajime Cinnamon Lab 10th Floor Geleximco Building 36 Hoang Cau Dong Da District Hanoi Viet Nam Hung Yen University of Technology and Education Hung Yen Viet Nam Le Quy Don Technical University Hanoi Viet Nam

ISBN: (纸本)9789811561672

This paper presents a practical approach to fine-grained information extraction. Through plenty of authors’ experiences in practically applying information extraction to business process automation, there can be found a couple of fundamental technical challenges: (i) the availability of labeled data is usually limited and (ii) highly detailed classification is required. The main idea of our proposal is to leverage the concept of transfer learning, which is to reuse the pre-trained model of deep neural networks, with a combination of common statistical classifiers to determine the class of each extracted term. To do that, we first exploit BERT to deal with the limitation of training data in real scenarios, then stack BERT with Convolutional Neural Networks to learn hidden representation for classification. To validate our approach, we applied our model to an actual case of document processing using a public data of competitive bids for development projects in Japan. We used 100 documents for training and testing and confirmed that the model enables to extract fine-grained named entities with a detailed level of information preciseness specialized in the targeted business process, such as a department name of application receivers. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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ATTENTION BASED SEMI-SUPERVISED DICTIONARY learning FOR DIAGNOSIS OF AUTISM SPECTRUM DISORDERS

ATTENTION BASED SEMI-SUPERVISED DICTIONARY LEARNING FOR DIAG...

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IEEE international conference on Multimedia and Expo (IEEE ICME)

作者： Yang, Meng Zhong, Qin Chen, Lin Huang, Fanglin Lei, Baiying Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China SYSU Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China Sogou Beijing Peoples R China Shenzhen Univ Guangdong Key Lab Biomed Measurements & Ultrasoun Sch Biomed EngnHlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China

ISBN: (纸本)9781538692141

Diagnosis of Autism spectrum disorder (ASD) has rapidly developed by using technologies of neuroimaging and machine learning. However, there are still two crucial issues that degrades the performance of existing diagnosis methods of ASD. On one hand, the potential relationships between different samples (e.g., patients and health controls) are ignored. The other challenge is that there are usually a limited number of labeled training samples in practical applications. In order to resolve above problems, in this paper we propose a novel attention based semi-supervised dictionary learning (ASSDL) for diagnosis of ASD by exploring the discrimination embedded in the unlabeled data. In the proposed ASSDL, the discriminative relationship hidden in the samples is exploited by learning a class-specific dictionary with designed class-commonality and sample-particularity representation regularizations;while for the unlabeled data, an attention scheme incorporating class-specific reconstruction error is proposed in the label estimation. A good class estimation of unlabeled data and a discriminative dictionary learning will benefit to each other, making the dictionary based classifier better and better. We present an efficient algorithm to minimize the derived objective function and the proposed method shows the promising performance on the four publicly available autism datasets.

关键词： Dictionary learning Semi-Supervised learning Autism Spectrum Disorder Self-Attention

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