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arXiv 2018年

作者： Kahrobaei, Delaram Mallahi-Karai, Keivan University of York Department of Computer Science United Kingdom City University of New York CUNY Graduate Center PhD Program in Computer Science New York United States New York University Computer Science and Engineering Department Department of mathematics and logistic Bremen28759 Germany

In this paper we will offer a new symmetric-key cryptographic scheme which is based on the existence of exponentially distorted subgroups in arithmetic groups. Aside from this, we will also provide new examples of distorted subgroups in SLn(Z[x]) which can be utilized for the same purpose. Copyright © 2018, The Authors. All rights reserved.

关键词： Cryptography

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Development of 5G Cyber-Physical Production System

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International Journal of Networked and Distributed Computing 2023年第1期11卷 9-19页

作者： Huang, Hsuan-Chao Tsai, Chun-Hsien Lin, Hsiung-Cheng Department of Computer Science and Information Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan PhD program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan

The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production control. However, OT and ICT are now getting more complex in the modern industry. To enhance industrial management and production process, the CPPS based on the 5G architecture under production environments can provide a more comprehensive solution in the context of Industry 4.0. For this purpose, a 5G CPPS model to improve the interoperability of CPPS in ICT and OT is proposed to build a virtualized Industry 4.0 asset management shell system. First, real-time task artifacts and 5G communication modules are bound in a certain form of virtualization. Second, task artifacts are released to the production line in a real-time scheduling. Third, an order is placed on a production line and split into multiple tasks for production process. Single-machine single-workpiece and multi-machine and multi-workpiece processing cases are used for testing in the production line. Experimental results show that the proposed 5G CPPS can efficiently achieve multi-machine work piece processing. © 2022, The Author(s).

关键词： 5G Cyber-physical production system (CPPS) Digital twin (DT) Industry 4.0 Information technology Operational technology

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Robust prediction of personalized in vivo response to unseen drugs from in vitro screens using a novel context-aware deconfounding autoencoder

Research Square

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Research Square 2021年

作者： He, Di Liu, Qiao Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient-specific responses to drug treatments is critical for drug development and personalized medicine. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell line data, few of them can reliably predict individual patient clinical responses to new drugs due to data distribution shift and confounding factors. We have developed a novel Context-aware Deconfounding Autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization, significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific in vivo drug responses purely from in vitro screens. Using CODE-AE, we screened 59 drugs for 9,808 cancer patients. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized anti-cancer therapies and drug-response biomarkers. © 2021, CC BY.

关键词： Cell culture

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The effect of node selfishness on the performance of WSN cluster-based routing algorithms

The effect of node selfishness on the performance of WSN clu...

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AFRICON Conference

作者： Ketema Adere Gemeda Gabriele Gianini Mulugeta Libsie IT PhD Program Addis Ababa University Ethiopia Dipartimento di Informatica Universitàdegli Studi di Milano Italy Department of Computer Science Addis Ababa University Ethiopia

In Wireless Sensor Networks (WSNs), energy efficiency is among the most important factors since the lifetime of an unattended node is limited by its battery lifetime. In infrastructure-less WSNs, in order to use their resources efficiently, nodes can be organized hierarchically. In this case, long range communication is entrusted to a subset of nodes, the Cluster Heads (CHs): each CH acts as a gateway between the local nodes in its cluster and the other CHs in the network. When in this role, a node must stand a heavier traffic and consume more resources: fairness dictates that the role has to be taken in turn by each node in an area that fits the task. However, a selfish node may adopt several strategies to save its energy: it can make itself unavailable for the role of CH, or, when in that role, it can avoid relaying packets. In this work, we study the impact of selfish nodes on the performance of the cluster based algorithm LEACH. By taking into consideration several Quality of Service metrics, we quantify the extent of performance degradation as a function of the percentage of selfish nodes. We show that, when the percentage of selfish nodes is 75%, only 37% of packets reach the base station, network lifetime reduces to 40% of rounds and packet retransmission attempts is close to 63%.

关键词： Wireless sensor networks Clustering algorithms Routing Algorithm design and analysis Energy efficiency Base stations Protocols

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CODE-AE: A coherent de-confounding autoencoder for predicting patient-specific drug response from cell line transcriptomics

arXiv

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arXiv 2021年

作者： He, Di Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient’s response to drug treatments is critical for developing precision medicine. However, it is often difficult to obtain a sufficient amount of coherent drug response data from patients directly for training a generalized machine learning model. Although the utilization of rich cell line data provides an alternative solution, it is challenging to transfer the knowledge obtained from cell lines to patients due to various confounding factors. Few existing transfer learning methods can reliably disentangle common intrinsic biological signals from confounding factors in the cell line and patient data. In this paper, we develop a Coherent Deconfounding Autoencoder (CODE-AE) that can extract both common biological signals shared by incoherent samples and private representations unique to each data set, transfer knowledge learned from cell line data to tissue data, and separate confounding factors from them. Extensive studies on multiple data sets demonstrate that CODE-AE significantly improves the accuracy and robustness over state-of-the-art methods in both predicting patient drug response and deconfounding biological signals. Thus, CODE-AE provides a useful framework to take advantage of in vitro omics data for developing generalized patient predictive models. The source code is available at https://***/XieResearchGroup/CODE-AE. © 2021, CC BY.

关键词： Learning systems

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AN ALGEBRAIC CHARACTERIZATION OF k–COLORABILITY

arXiv

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arXiv 2020年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York UK New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Mathematics Department University of Virginia CharlottesvilleVA22904

We characterize k–colorability of a simplicial graph via the intrinsic algebraic structure of the associated right-angled Artin group. As a consequence, we show that a certain problem about the existence of homomorphisms from right-angled Artin groups to products of free groups is NP–complete. Copyright © 2020, The Authors. All rights reserved.

关键词： Algebra

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Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

Speech Emotion Recognition using Convolutional Neural Networ...

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International Symposium on Chinese Spoken Language Processing

作者： Kun-Yi Huang Chung-Hsien Wu Qian-Bei Hong Ming-Hsiang Su Yuan-Rong Zeng Department of Computer Science and Information Engineering National Cheng Kung University Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781538656280;9781538656273

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.

关键词： Emotion recognition Feature extraction Speech recognition Acoustics Training Databases Neural networks

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To Sharpen or not to Sharpen? On the Effect of Sharpening Filters Applied to Magnified Images 14

To Sharpen or not to Sharpen? On the Effect of Sharpening Fi...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Emmendorfer, Leonardo R. de Olviveira, Vinicius Menezes Schein, Tatiana T. de Jesus, Junior C. Rodriguez, Bárbara D.A. Electrical Energy Processing Department Federal University of Santa Maria 97105-340 Brazil Center for Computational Science Federal University of Rio Grande 96201-900 Brazil Computational Modeling PhD Program Federal University of Rio Grande 96201-900 Brazil Computer Engineering Graduate Program Federal University of Rio Grande 96201-900 Brazil Institute of Mathematics Statistics and Physics Federal University of Rio Grande 96201-900 Brazil

ISBN: (纸本)9781510657564

Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual Image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent. © 2022 SPIE.

关键词： Quality control

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A Model and Tool for Community Engagement Case Study: Community Engagement in the Bisotun World Heritage Site

arXiv

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arXiv 2022年

作者： Nasrolahi, Ahmad Messina, Vito Gena, Cristina Tech4Culture Phd Program University of Turin Turin Italy Historical Studies Dept University of Torino Turin Italy Computer Science Dept University of Torino Turin Italy

Participating local community in cultural heritage management has always been a concern since the Venice Charter so far (1964). In addition, the Faro Convention (2005) shifted focus from cultural heritage values to the values of cultural heritage for society. In this case, there is required to design a simple method and tool in order to facilitate the interaction between local people and authority. This paper introduces a method and tool (iCommunity) that are now using in community-centered approach in the Bisotun World Heritage Site. The main goal of this application is to find a simple and applicable method and tool for inclusion the local community in decision-making processes in cultural heritage management. The proposed model is designed based on the general public participation principles, on the one hand, and the users’ need and requirement in the world heritage site, on the other hand. © 2022, CC BY.

关键词： Decision making

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification

Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

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IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Shu Liu Qiang Wu Xin Yang Computational and Data Science PhD Program Middle Tennessee State University Murfreesboro TN USA Department of Mathematics University of Tennessee Knoxville TN USA Department of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method.

关键词： Training Support vector machines Neuroimaging Autism Machine learning algorithms Functional magnetic resonance imaging Vectors Robustness Classification algorithms Sustainable development

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