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2nd Machine Learning for Health Symposium, ML4H 2022

作者： Mao, Haiyi Liu, Hongfu Xiaotian Dou, Jason Benos, Panayiotis V. Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh United States Michtom School of Computer Science Brandeis University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Epidemiology University of Florida United States

Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an IDH-mutation cause differences in patients' MRI images? Conventional causal discovery algorithms, although well developed to uncover the cause-effect relationships on structured data, cannot elucidate causal relations between unstructured images and structured scalar variables due to the complexity of the former. In this paper, we consider causal discovery between images and structured (scalar) variables. Specifically, we derive low dimensional image representations to analyze with structured variables. We propose a two-module amortized variational algorithm named Cross-Modal Variational Causal representation and structure Learning (CMCL). CMCL jointly learns identifiable representations given a set of independent structured variables and causal relations via formulating latent representations and structured variables into a direct acyclic graph. Moreover, we further enforce counterfactual invariance/variance onto representations. We demonstrate that CMCL outperforms other related methods on synthetic datasets and validate causal relations on semi-synthetic datasets by visualization. © 2022 P.N. Argaw, E. Healey & I.S. Kohane.

关键词： Coronavirus

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Amharic Semantic Information Retrieval System 1

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12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2020

作者： Yeshambel, Tilahun Mothe, Josiane Assabie, Yaregal IT PhD Program Addis Ababa University Addis Ababa Ethiopia INSPE Univ. de Toulouse IRIT UMR5505 CNRS Toulouse France Department of Computer Science Addis Ababa University Addis Ababa Ethiopia

ISBN: (数字)9783031146022

ISBN: (纸本)9783031146015

Amharic is the official language of Ethiopia, currently having a population of over 118 million. Developing effective information retrieval (IR) system for Amharic has been a challenging task due to limited resources coupled with complex morphology of the language. This paper presents the development of Amharic semantic IR system using query expansion based on deep neural learning model and WordNet. In order to optimize the retrieval result, we propose Amharic text representation using root forms of words applied for stopword identification, indexing, term matching and query expansion. Comparisons are made with the conventional stem-based text representation for information retrieval, and we show that using the root forms of words is better for both resource construction and system development. The effectiveness of the proposed Amharic semantic IR system is evaluated on Amharic Adhoc Information Retrieval Test Collection (2AIRTC). © 2022, Springer Nature Switzerland AG.

关键词： Information retrieval

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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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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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Spleen-preserving distal pancreatectomy from multi-port to reducedport surgery approach

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World Journal of Gastrointestinal Surgery 2023年第7期15卷 1501-1511页

作者： Ching-Lung Hsieh Tung-Sheng Tsai Cheng-Ming Peng Teng-Chieh Cheng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724TaiwanChina Department of Surgery Chung Shan Medical University HospitalTaichung 40201TaiwanChina PhD Program of Electrical and Communications Engineering Feng Chia UniversityTaichung 40724Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Da Vinci Minimally Invasive Surgery Center Chung Shan Medical University HospitalTaichung 40201Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

BACKGROUND Minimally invasive pancreatic surgery via the multi-port approach has become a primary surgical method for distal pancreatectomy(DP)due to its advantages of lower wound pain and superior cosmetic *** studies have applied reduced-port techniques for DP in an attempt to enhance cosmetic outcomes due to the minimally invasive *** recent review studies have compared multi-port laparoscopic DP(LDP)and multi-port robotic DP(RDP);most of these studies concluded multi-port RDP is more beneficial than multi-port LDP for spleen ***,there have been no comprehensive reviews of the value of reduced-port LDP and reduced-port *** To search for and review the studies on spleen preservation and the clinical outcomes of minimally invasive DP that compared reduced-port DP surgery with multi-port DP *** The PubMed medical database was searched for articles published between 2013 and *** search terms were implemented using the following Boolean search algorithm:(“distal pancreatectomy”OR“left pancreatectomy”OR“peripheral pancreatic resection”)AND(“reduced-port”OR“single-site”OR“single-port”OR“dual-incision”OR“single-incision”)AND(“spleen-preserving”OR“spleen preservation”OR“splenic preservation”).A literature review was conducted to identify studies that compared the perioperative outcomes of reduced-port LDP and reduced-port *** Fifteen articles published in the period from 2013 to 2022 were retrieved using three groups of search *** studies were added after manually searching the related ***,10 papers were selected after removing case reports(n=3),non-English language papers(n=1),technique papers(n=1),reviews(n=1),and animal studies(n=1).The common items were defined as items reported in more than five papers,and data on these common items were extracted from all *** ten studies included a total of 337 patients(females/males:231/106)who underwent *** total,166 patients(females/male

关键词： Minimally invasive surgery Robotic distal pancreatectomy Laparoscopic distal pancreatectomy Spleen preservation Reduced-port Multi-port

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Evaluation of Corpora, Resources and Tools for Amharic Information Retrieval 8th

Evaluation of Corpora, Resources and Tools for Amharic Infor...

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8th EAI International Conference on Advancement of science and Technology, ICAST 2020

ISBN: (纸本)9783030806200

Amharic is the working language of Ethiopia. It is the second-most commonly spoken Semitic language in the world next to Arabic. Amharic is morphologically complex and under-resourced, which poses tremendous challenges for natural language processing. The development of fully functional Amharic text processing applications is a non-trivial task for researchers and developers. Despite attempts to develop some applications, lack of standards in corpus collection and resource development resulted in the problem of interoperability. The aim of this paper is to present and evaluate the accessibility of Amharic corpora, resources and tools with the purpose of highlighting the status of Amharic language processing applications. We present available resources and linguistic tools, assess their usability and effectiveness, investigate the implications of the morphological complexity and put the way forward in the development of Amharic text processing applications. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Text processing

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Construction of Morpheme-Based Amharic Stopword List for Information Retrieval System 8th

Construction of Morpheme-Based Amharic Stopword List for Inf...

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8th EAI International Conference on Advancement of science and Technology, ICAST 2020

ISBN: (纸本)9783030806200

One of the major forms of pre-processing in information retrieval and many other text processing applications is filtering out stopwords. They are ignored by many retrieval systems during indexing and retrieval in order to enhance retrieval effectiveness and efficiency. The aim of this paper is to present the construction of morpheme-based Amharic stopwords and investigate their effect on information retrieval tasks. The stopword list is constructed based on the semantics of Amharic words and corpus statistics: frequency, mean, variance, and entropy parameters. The stopword list is evaluated using Lemur on Amharic information retrieval test collection. Removal of stopwords has shown significant impact on retrieval effectiveness, size of index and term weighting of non-stopwords. On the other hand, their presence in index and query negatively affects the retrieval effectiveness of Amharic retrieval system. The average precisions of retrieving with and without stopwords using language modeling on root-based approach are 0.24 and 0.70, respectively. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Semantics

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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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BRAIN-TO-TEXT BENCHMARK’24: LESSONS LEARNED

arXiv

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arXiv 2024年

作者： Willett, Francis R. Li, Jingyuan Le, Trung Fan, Chaofei Chen, Mingfei Shlizerman, Eli Chen, Yue Zheng, Xin Okubo, Tatsuo S. Benster, Tyler Lee, Hyun Dong Kounga, Maxwell Buchanan, E. Kelly Zoltowski, David Linderman, Scott W. Henderson, Jaimie M. Department of Neurosurgery Stanford University United States Department of Electrical & Computer Engineering University of Washington United States Department of Computer Science Stanford University United States Department of Applied Mathematics University of Washington United States China Neurosciences Phd Program Stanford University United States Wu Tsai Neurosciences Institute Department of Statistics Stanford University United States

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark’24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms. © 2024, CC BY.

关键词： Recurrent neural networks

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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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