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An In-depth Analysis of Graph Neural Networks for Semi-supervised learning 1

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9th Joint international Semantic Technology conference (JIST)

作者： Chen, Yuyan Hu, Sen Zou, Lei Peking Univ Beijing Peoples R China

ISBN: (数字)9789811534126

ISBN: (纸本)9789811534126;9789811534119

Graph Neural Networks have experienced a rapid development in the last few years and become powerful tools for many machine learning tasks in graph domain. Graph Convolution Network is a breakthrough and become a strong baseline for node classification task. To this end, we perform a thorough experiment for several prominent GCN-related models, including GAT, AGNN, Co-Training GCN and Stochastic GCN. We found that different models take their advantages in different scenarios, depending on training set size, graph structure and datasets. Through our in-depth analysis of attention mechanism, dataset splits and the preprocessing for knowledge graphs, we report some interesting findings. And we look into GCNs for knowledge graphs carefully, then propose a new scheme for data processing, which achieves a better performance compared to traditional methods.

关键词： GCN Attention mechanism Knowledge graph

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VAWS: Vulnerability Analysis of Neural Networks using Weight Sensitivity 62

VAWS: Vulnerability Analysis of Neural Networks using Weight...

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62nd IEEE international Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Hailesellasie, Muluken Nelson, Jacob Khalid, Faiq Hasan, Syed Rafay Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA Vienna Univ Technol TU Wien Inst Comp Engn Vienna Austria

ISBN: (纸本)9781728127880

The advancement in deep learning has taken the technology world by storm in the last decade. Although, there is enormous progress made in terms of algorithm performance, the security aspect of these algorithms has not received a lot of attention from the research community. As more industries start to adopt these algorithms the issue of security is becoming even more relevant. Security vulnerabilities in machine learning (ML), especially in deep neural networks (DNN), is becoming a concern. Various techniques have been proposed, including data manipulations and model stealing. However, most of them are focused on ML algorithms and target threat models that require access to training dataset. In this paper, we present a methodology that analyzes the DNN weight parameters under the threat model that assumes the attacker has the access to the weight memory only. This analysis is then used to develop an attack that manipulates weight parameters with respect to their sensitivity. To evaluate this attack, we implemented our methodology on a MLP trained on IRIS dataset and LeNet (DNN architecture) trained on MNIST dataset. Our experimental results demonstrate that alteration of model parameters results in subtle accuracy drop of the model. Depending on the applications such subtle changes can cause significant system malfunction or disruption, for example in vision-based industrial applications. Our results show that using our methodology a subtle accuracy drop can be achieved in a reasonable amount of time with very few parameter changes.

关键词： machine learning Deep learning DNN Security Weight Sensitivity Convolutional Neural Networks CNN

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Identifying Candidates for Medical Coding Audits: Demonstration of a data Driven Approach to Improve Medicare Severity Diagnosis-Related Group Coding Compliance 8th

Identifying Candidates for Medical Coding Audits: Demonstrat...

引用

8th international conference on Health Information science (HIS)

作者： Feng, Yunyi Lin, Simon Lin, En-Ju Farley, Lesley Huang, Yungui Liu, Chang Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA Nationwide Childrens Hosp Res Inst Columbus OH USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA

ISBN: (纸本)9783030329624;9783030329617

Correct code assignment of Medicare Severity Diagnosis-Related Group (MS-DRG) is critical for healthcare. However, there is a gap currently on automatically identifying all potentially miscoded cases and prioritizing manual reviews over these cases. This paper reports a new process using a data-driven machine learning approach to flag potentially misclassified cases for manual review. We investigated using a stack of regularized logistic/softmax regression, random forest, and support vector machine to suggest potential cases for manual review by care providers, provided details addressing the data imbalance, and explored using features from various source including diagnosis and procedure codes, length of stay and the access log data from the electronic health record system. This potentially improves the efficiency of the coding review by care providers, providing another line of defense against miscoding to enhance coding compliance, and reduce the negative effects of upcoding and down-coding. We tested the new method with four common pediatric conditions and demonstrated its feasibility.

关键词： Medical coding audit MS-DRG

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Various Patient classification model using ABR data

Various Patient classification model using ABR data

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2022 international conference on Computational science and Computational Intelligence, CSCI 2022

作者： Ma, Jun Choi, Seong Jun Kim, Jaehyoun Hong, Min Soonchunhyang University Department of Software Convergence Asan Korea Republic of Soonchunhyang University Cheonan Hospital Department of Otorhinolaryngology-Head and Neck Surgery College of Medicine Cheonan Korea Republic of Sungkyunkwan University Department of Computer Education Seoul Korea Republic of Soonchunhyang University Department of Computer Software Engineering Asan Korea Republic of

ISBN: (纸本)9798350320282

Auditory brain response (ABR) is an auditory test developed in the 1970s that enables objective testing regardless of age. However, ABR has the disadvantage that analysis and reading are essential because it is complicated to inspect and requires trained inspectors. In this paper, we present a deep learning Keras sequential model and a VGG16 model that distinguish between deaf patients and normal people using ABR data and compare the performance and accuracy of the model. It is expected that a deep learning model that automatically distinguishes deaf patients from normal people will be applied in the future using the proposed model. © 2022 IEEE.

关键词： Deep learning

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Generative models and model criticism via optimized maximum mean discrepancy 5

Generative models and model criticism via optimized maximum ...

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5th international conference on learning Representations, ICLR 2017

作者： Sutherland, Dougal J. Tung, Hsiao-Yu Strathmann, Heiko De, Soumyajit Ramdas, Aaditya Smola, Alex Gretton, Arthur Gatsby Computational Neuroscience Unit University College London United Kingdom School of Computer Science Carnegie Mellon University United States Departments of EECS and Statistics University of California at Berkeley United States

We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples. In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples. Second, the MMD can be used to evaluate the performance of a generative model, by testing the model's samples against a reference data set. In the latter role, the optimized MMD is particularly helpful, as it gives an interpretable indication of how the model and data distributions differ, even in cases where individual model samples are not easily distinguished either by eye or by classifier. This post-publication revision corrects some errors in constants of the estimator (5). The appendix deriving the estimator has been replaced by Sutherland (2019). © ICLR 2019 - conference Track Proceedings. All rights reserved.

关键词： Generative adversarial networks

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The status of information security systems in banking sector from social engineering perspective 2

The status of information security systems in banking sector...

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2nd international conference on data science, E-learning and Information Systems, data 2019

作者： Hammour, Rand Abu Gharaibeh, Yousef Al Qasaimeh, Malik Al-Qassas, Raad S. PSUT Amman Jordan

ISBN: (纸本)9781450372848

Social Engineering Attack has recently become a real threat affecting organizations, and 53.9% of such attacks target the banking sector. Successful attacks violate privacy by breaching sensitive data, and can cause huge financial loss for organizations and individuals, alongside reputational damage for firms. Although banks invest extensive resources in cyber security, with large budgets spent on securing their hardware and software, the human factor offers numerous weaknesses that can be easily exploited, and real and pertinent security challenges remain serious threats. This paper presents an information technology governance framework applied on a Jordanian bank to protect the system from social engineering attack. We worked on a case study that mainly focuses on phishing attack, which is considered one of the most common threats in banks, and the way staff will deal with it. The results show positive improvements in staff awareness and in avoiding such types of attacks, as well as a marked increase in reporting any suspicious activity noticed by employees. © 2019 Association for Computing machinery. ACM

关键词： Sensitive data

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Development of CNC machining II textbooks to increase the effectiveness of student learning in the mechanical engineering education study program

Development of CNC machining II textbooks to increase the ef...

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2019 international conference on science Education and Technology, ICOSETH 2019

作者： Muslim Basuki, Nur Riadi, Selamat Indonesia

This research is research development. The research model used in this research is the development of 4D (Four-D Model) which consists of four stages: the defining stage, the design stage, and the development phase. While the dissemination phase was not carried out because this research was only limited to the feasibility test and the effectiveness of learning media in the form of books. In this study, the respondents were 3 book experts, 3 lecturers who had CNC machine expertise competence in the Department of Mechanical Engineering Education, Faculty of Engineering, State University of Medan. The research instrument used to collect data in the form of questionnaire sheets and observation sheets. data analysis was performed to determine the feasibility and effectiveness of the resulting CNC II/advanced book. From the results of this development research, it was shown that the developed CNC II/advanced book was suitable for use in learning advanced CNC courses. The results of book validation by book experts amounted to 86.67%, CNC exports amounted to 95.80.% Included in the very feasible criteria. The effectiveness of the advanced CNC learning book that was developed already meets the requirements of learning media with a percentage of positive student responses to the book of 96.11% of the criterion score, as well as an increase in student learning activities expressed in percentage. © Published under licence by IOP Publishing Ltd.

关键词： Students

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A Cross-Culture Study on Multimodal Emotion Recognition Using Deep learning 26th

A Cross-Culture Study on Multimodal Emotion Recognition Usin...

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26th international conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Gan, Lu Liu, Wei Luo, Yun Wu, Xun Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Loke Comp & Machine Intelligence Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Shanghai Educ Commiss Intelligent Interact & Cogn Shanghai Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Shanghai Peoples R China

ISBN: (纸本)9783030368081;9783030368074

In this paper, we aim to investigate the similarities and differences of multimodal signals between Chinese and French on three emotions recognition task using deep learning. We use videos including positive, neutral and negative emotions as stimuli material. Both Chinese and French subjects wear electrode caps and eye tracking glass while doing experiments to collect electroencephalography (EEG) and eye movement data. To deal with the problem of lacking data for training deep neural networks, conditional Wasserstein generative adversarial network is adopted to generate EEG and eye movement data. The EEG and eye movement features are fused by using Deep Canonical Correlation Analysis to analyze the relationship between EEG and eye movement data. Our experimental results show that French has higher classification accuracy on beta frequency band while Chinese performs better on gamma frequency band. In addition, EEG signals and eye movement data of French participants have complementary characteristics in discriminating positive and negative emotions.

关键词： Emotion recognition EEG Eye movement Deep learning Cross-culture Chinese French

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Generative Creativity: Adversarial learning for Bionic Design 28th

Generative Creativity: Adversarial Learning for Bionic Desig...

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28th international conference on Artificial Neural Networks (ICANN)

作者： Yu, Simiao Dong, Hao Wang, Pan Wu, Chao Guo, Yike Imperial Coll London London England Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9783030305086;9783030305079

Generative creativity in the context of visual data refers to the generation process of new and creative images by composing features of existing ones. In this work, we aim to achieve generative creativity by learning to combine spatial features of images from different domains. We focus on bionic design as an ideal task for this study, in which a target object (e.g. a floor lamp) is designed to contain features of biological source objects (e.g. flowers), resulting in creative biologically-inspired design. Specifically, given an input image of a design target object, a generative model should learn to generate images that (1) maintain shape features of the input design target image, (2) contain shape features of images from the specified biological source domain, (3) are plausible and diverse. We propose DesignGAN, a novel unsupervised deep generative approach to realising shape-oriented bionic design. DesignGAN employs an adversarial learning architecture with designated losses to generate images that meet the three aforementioned requirements of bionic design modelling. We perform qualitative and quantitative experiments to evaluate our method, and demonstrate that our proposed framework successfully generates creative images of bionic design.

关键词： Adversarial learning Bionic design Image generation Unsupervised learning Representation learning

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Handwritten Numerals' Recognition in Kurdish Language Using Double Feature Selection 2

Handwritten Numerals' Recognition in Kurdish Language Using ...

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2nd international conference on Engineering Technology and its Applications, IICETA 2019

作者： Abdulrazzaq, Maiwan Bahjat Khalaf, Khalid Ibrahim University of Zakho Department of Computer Science Duhok Iraq

ISBN: (纸本)9781728141053

In the contemporary rapidly developing world, machine learning and pattern recognition has achieved a special level of attention by the academicians and international researchers. Such methods have economized time and increased the speed of processing tons of data in a single click. Pattern recognition is being used in a broad context, especially in industry, finance and banking, forensic issues, etc. Even though pattern selection programs have economized our time in an eye-catching way, yet choosing the best and most accurate algorithm is still a challenge for many researchers. In this paper, a Double Feature Selection (DFS) method as a new trend, using Correlation and ReliefF, is being elaborated in order to appraise the ratio of digit recognition accuracy, instead of using them individually. For this purpose, a comparison is implied to illustrate the accuracy of the four algorithms;Decision Tree (J48), KNN, Multi-Layer Perceptron (MLP), and Naïve Bayes (NB) as far as they are concerned with the digits and numerals used in Kurdish Language. The result of the study illustrates that DT has the best accuracy with the ratio of 95.6%. © 2019 IEEE.

关键词： Decision trees

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