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The distribution problem of unstructured data when solving data mining tasks on computer clusters 1

The distribution problem of unstructured data when solving d...

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1st international Conference of Information Systems and Design, ICID 2019

作者： Zein, Ali Najyevich Borisova, Svetlana Vyacheslavovna Moscow Power Engineering Institute National Research University Krasnokazarmennaya 14 Moscow Russia

In the modern world, the amount of information is growing exponentially, with every minute new data appears for further storage, processing and *** data sources generate huge amounts of data and most of them are presented in a format that does not correlate well with traditional structured database formats. The three main "commandments" of big data (3V) are Volume (large amount of information), Variety (heterogeneity and lack of structure) and Velocity (data processing speed) [1]. Imagine that there is a large amount of published information that needs to be sorted into sections, which will greatly speed up and simplify its reading and processing [2]. By machine learning can be attributed various methods of cluster analysis, with which you can automate the classification by finding patterns or patterns that describe this data. Using the method of cluster analysis of "K-means", it is possible to achieve the distribution of the K-number of clusters at the greatest possible distance from each other, thereby allowing us to assess how much they differ. Ideally, the differences will be quite large, which will show the quality of execution, thereby significantly accelerating the process of analysis and information content. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： data mining

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Unsupervised Representation learning with Attention and Sequence to Sequence Autoencoders to Predict Sleepiness from Speech 1

Unsupervised Representation Learning with Attention and Sequ...

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1st international Multimodal Sentiment Analysis in Real-Life Media Challenge and workshop, MuSe 2020

作者： Amiriparian, Shahin Winokurow, Pawel Karas, Vincent Ottl, Sandra Gerczuk, Maurice Schuller, Björn Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Augsburg Germany

ISBN: (纸本)9781450381574

Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence to sequence autoencoders for fully unsupervised representation learning from audio files. In particular, we test the efficacy of our novel approach on the task of speech-based sleepiness recognition. We evaluate the learnt representations from both autoencoders, and conduct an early fusion to ascertain possible complementarity between them. In our frameworks, we first extract Mel-spectrograms from raw audio. Second, we train recurrent autoencoders on these spectrograms which are considered as time-dependent frequency vectors. Afterwards, we extract the activations of specific fully connected layers of the autoencoders which represent the learnt features of spectrograms for the corresponding audio instances. Finally, we train support vector regressors on these representations to obtain the predictions. On the development partition of the data, we achieve Spearman's correlation coefficients of .324, .283, and .320 with the targets on the Karolinska Sleepiness Scale by utilising attention and non-attention autoencoders, and the fusion of both autoencoders' representations, respectively. In the same order, we achieve .311, .359, and .367 Spearman's correlation coefficients on the test data, indicating the suitability of our proposed fusion strategy. © 2020 ACM.

关键词： learning systems

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

1st International Conference on Computational Intelligence i...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

ISBN: (纸本)9789811390418

The proceedings contain 88 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Classification of Speech and Song Using Co-occurrence-Based Approach;Intelligent Analysis for Personality Detection on Various Indicators by Clinical Reliable Psychological TTH and stress Surveys;automatic Multilingual System from Speech;an Improved data Hiding Scheme Through Image Interpolation;generalization of Multi-bit Encoding Function Based data Hiding Scheme;a machine learning Approach to Comment Toxicity Classification;a Minutia Detection Approach from Direct Gray-Scale Fingerprint Image Using Hit-or-Miss Transformation;face Identification via strategic Combination of Local Features;a study on Reversible Image Watermarking Using Xilinx System Generator;a Novel Enhancement and Segmentation of Color Retinal Image Based on Fuzzy Measure and Fuzzy Integral;SVM and MLP Based Segmentation and recognition of Text from Scene Images Through an Effective Binarization Scheme;user Identification and Authentication Through Voice Samples;template Matching for Kinship Verification in the Wild;Attack Prevention Scheme for Privacy Preservation (APSP) Using K Anonymity in Location Based Services for IoT;statistical Analysis of the UNSW-NB15 dataset for Intrusion Detection;Singer Identification Using MFCC and CRP Features with Support Vector machines;object Proposals Based on Variance Measure;deep learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen;Robust Non-blind Video Watermarking Using DWT and QR Decomposition;detection and Classification of Earthquake Images from Online Social Media;On the Cryptanalysis of S-DES Using Binary Cuckoo Search Algorithm;numerical Integration Based Contrast Enhancement Using Simpson’s Method;self Driving Car.

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Search Space Complexity of Iteration Domain Based Instruction Embedding for Deep learning Accelerators 2nd

Search Space Complexity of Iteration Domain Based Instructio...

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2nd international workshop on IoT streams for data-Driven Predictive Maintenance, IoT streams 2020, and 1st international workshop on IoT, Edge, and Mobile for Embedded machine learning, ITEM 2020, co-located with ECML/PKDD 2020

作者： Rieber, Dennis Fröning, Holger Robert Bosch GmbH Stuttgart Germany Computing Systems Group Heidelberg University Heidelberg Germany

ISBN: (纸本)9783030667696

With the success of deep learning applications in many domains, the number of new deep learning operators and hardware accelerators increased significantly in recent years. Building efficient deep learning kernels from complex machine instructions like matrix-multiply is a task not yet automated by tools. Deciding which loop and memory transformations to perform and which part of the computation to implement with which machine instruction is mostly done by a human expert. Automating this task presents two challenges: 1) finding the right combination of transformations and 2) deciding where to embed which available instruction. Current work, such as ISAMIR [16] or TVM [3], offer some degree of automation for this task on iteration domain granularity and report results competitive with performance libraries. In this work we explore the search space complexity of this task with a novel graph-based Intermediate Representation (IR) that allows a hierarchical description of the n-dimensional tensor computations performed by Deep learning operators. We create an extensive search space across possible transformations of a given computation and then try to find possible implementations in a state of the art Deep learning instruction set using pattern matching. Our results show that further research into the design of embedding methods for specialized instruction is required to gracefully handle the vast search space of possible transformations. © 2020, Springer Nature Switzerland AG.

关键词： Tensors

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An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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Artificial Intelligence and data Analytics (CAIDA), international Conference on

作者： Rabia Javed Tanzila Saba Salman Humdullah Nor Shahida Mohd Jamail Mazhar Javed Awan Lahore College for Women University Lahore Pakistan Computer and Information Science Prince Sultan University Riyadh Saudi Arabia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru University of Management and Technology Lahore Pakistan

ISBN: (纸本)9781665430418

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier.

关键词： Drugs Dictionaries data analysis data preprocessing Random forests pattern matching Medical diagnostic imaging

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Building a feature taxonomy of the terms extracted from a text collection 1

Building a feature taxonomy of the terms extracted from a te...

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Moiseyenko, Svitlana Vasileyko, Alexander Ermolayev, Vadim Department of Computer Science Zaporizhzhia National University Zhukovskogo st. 66 Zaporizhzhia Ukraine

This position paper presents an approach for feature grouping and taxonomic relationship extraction with the further objective to build a feature taxonomy of a learned ontology. The approach needs to be developed as a part of the OntoElect methodology for domain ontologies refinement. The paper contributes a review of the related work in taxonomic relationships extraction from natural language texts. Within this review, the research gaps and remaining challenges are analyzed. The paper proceeds with outlining the envisioned solution. It presents the approach to this solution starting with the research questions, followed by the initial research hypotheses to be tested. Consequently, the plan of research is presented, including the potential research problems, the rationale to use and re-use existing components, and evaluation plan. Finally, the proposed solution, and the project, are placed in the broader context of the overall OntoElect workflow. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Ontology

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Generation of memes to engage audience in social media 1

Generation of memes to engage audience in social media

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Kurochkin, Andrew Bokhan, Kostiantyn Ukrainian Catholic University Applied Sciences Faculty Lviv Ukraine Whirl Software Kyiv Ukraine

In digital marketing, memes have become an attractive tool for engaging online audience. Memes have an impact on buyers and sellers online behavior and information spreading processes. Thus, the technology of generating memes is a significant tool for social media engagement. The primary purpose of the project is to develop a new approach and compare it to the existing baselines in the field of social media content generation, more precisely - meme generation. A meme is an image superimposed with text, which has humoristic or sarcastic sense;a meme is just another type of visual online content. This project is aimed at applying state of the art Deep learning techniques as Transformer architecture to the meme generation problem. To achieve project objectives, we are going to collect dataset;create a model for generation of memes and titles based on the input text;create a model for defining optimal time to make a post;measure and analyze system performance in terms of social network audience engagement. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Deep learning

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Determining sentiment and important properties of Ukrainian language user reviews 1

Determining sentiment and important properties of Ukrainian ...

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Babenko, Dmytro Dyomkin, Vsevolod Ukrainian Catholic University Lviv Ukraine

The main idea of this project to develop a model, which can recognize emotions from user review text. Many websites contains review text with marks (stars or another representation) about some products or services. Sometimes user can write only text without a mark, in this case, such a model can help us to understand the estimation of this user. It can be a review of some special product, about booking a hotel or renting a car or something like that. Furthermore, another purpose of this project to detect which characteristics were influenced for such review. Here, the model should detect sentiments from text and classify for positive and negative. Moreover, it will understand which reasons were caused by these emotions and why the user wrote such texts. As this project mostly works with text processing, it is classical natural language processing task using deep learning. Probably, as a result, it would be some neural network, which was selected as the best from several experiments. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Deep learning

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Investigation of the complex data distributions for their efficient generation 1

Investigation of the complex data distributions for their ef...

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Kofman, Philipp Dobosevych, Oles Hryniv, Rostyslav Ukrainian Catholic University Lviv Ukraine University of Rzeszw Rzeszw Poland

Currently, the active development of image processing methods requires large amounts of correctly labeled data. The lack of quality data makes it impossible to use various machine learning methods. In case of limited possibilities for collecting real data, used methods for their synthetic generation. In practice, we can formulate the task of the high-quality generation of synthetic images as an efficient generation of complex data distributions, which is the object of study of this work. Generating high-quality synthetic data is an expensive and complicated process in terms of existing methods. We can distinguish two main approaches that are used to generate synthetic data: image generation based on rendered 3-D scenes and the use of GANs for simple images. These methods have some drawbacks, such as a narrow range of applicability and insufficient distribution complexity of the obtained data. When using GANs to generate complex distributions, in practice, we face a visible increase in the complexity of the model architecture and training procedure. A deep understanding of the real data complex distributions can be used to improve the quality of synthetic generation. Minimizing the differences in the real and synthetic data distributions can improve not only the generation process but also develop tools for solving the problem of data lack in the field of image processing. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Generative adversarial networks

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1st international Congress on Blockchain and Applications, BLOCKCHAIN 2019

1st International Congress on Blockchain and Applications, B...

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1st international Congress on Blockchain and Applications, BLOCKCHAIN 2019

ISBN: (纸本)9783030238124

The proceedings contain 21 papers. The special focus in this conference is on Blockchain and Applications. The topics include: Anticipatory policy as a design challenge: Experiments with stakeholders engagement in blockchain and distributed ledger technologies (bdlts);the electronic bill of lading: Challenges of paperless trade;a methodology for a probabilistic security analysis of sharding-based blockchain protocols;the “Tokenization” of the eparticipation in public governance: An opportunity to hack democracy;blockchain approach to solve collective decision making problems for swarm robotics;prediction of transaction confirmation time in ethereum blockchain using machine learning;improving event monitoring in iot network using an integrated blockchain-distributed pattern recognition scheme;on value preservation with distributed ledger technologies, intelligent agents, and digital preservation;blockchain technology: A review of the current challenges of cryptocurrency;lightning network: A comparative review of transaction fees and data analysis;a blockchain- and ai-based platform for global employability;blockchain and biometrics: A first look into opportunities and challenges;do smart contract languages need to be turing complete?;towards integration of blockchain and iot: A bibliometric analysis of state-of-the-art;clinicappchain: A low-cost blockchain hyperledger solution for healthcare;smart contracts are more than objects: Pro-activeness on the blockchain;blockchain based informed consent with reputation support;privacy centric collaborative machine learning model training via blockchain;fuzzy rules based solution for system administration security management via a blockchain.

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