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arXiv 2023年

作者： Venkat, Aarthi Chew, Joyce Rodriguez, Ferran Cardoso Tape, Christopher J. Perlmutter, Michael Krishnaswamy, Smita Program for Computational Biology and Bioinformatics Yale University United States Department of Mathematics University of California Los Angeles United States Department of Oncology University College London Cancer Institute United Kingdom Department of Mathematics Boise State University United States Department of Genetics and Computer Science Yale University United States

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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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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Industrial revolution 4.0 on big data adoption factors on social media users and popularity brands 2

Industrial revolution 4.0 on big data adoption factors on so...

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2nd International Conference on Lesson Study of science Technology Engineering and mathematics 2020, ICOLSSTEM 2020

作者： Sardjono, W. Rama Putra, G. Selviyanti, E. Cholidin, A. Salim, G. Information Systems Management Department Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia Health Department Politeknik Negeri Jember Jember Jawa Timur68124 Indonesia Law Studi Program Faculty of Law University of Muhammadiyah Jakarta Indonesia Post Graduate Program Faculty of Economic Science University of Trisakti Jakarta Indonesia

The purpose of writing this paper is to look at how factors of big data adoption affect the users of social media and brand popularity. The method used in the writing of this paper is by using path analysis and also by doing literature study to find reference material. The results of the analysis indicate that there is influence of big data adoption factors that affect the popularity of the brand. With this research, we get big data model that influence the brand popularity. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Big data

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eVision: Forecasting the spread of Tuberculosis in India with Deep Learning

eVision: Forecasting the spread of Tuberculosis in India wit...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Juan Zuluaga Michael Castillo Divya Syal Andres Calle Navid Shaghaghi Department of Bioengineering (BIOE) Computer Science & Engineering (CSEN) Ethical Pragmatic & Intelligent Computing (EPIC) Laboratory in collaboration with the Healthcare Innovation & Design (HID) Program Information Systems & Analytics (ISA) and Mathematics & Computer Science (MCS) Santa Clara University Santa Clara California USA

Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein officially presented the characteristics of it. Even though the TB epidemic has touched all corners of the world, Africa and Asia are the regions that currently suffer the worst consequences. The purpose of this study is to construct a model within the eVision forecasting environment, capable of forecasting the trend of Tuberculosis cases in India, as India is the country that accounts for the largest percentage of TB cases and deaths worldwide. And being able to make predictions for India may also lead to successful results for other regions in Asia and Africa. In order to do so, this study presents different test cases that show the effectiveness of the model, varying the number of steps for each one of the data sets created. It's important to note, that these data sets are combinations of data gathered from the states with the most TB cases in India in the last years, as well as the total data for India, and supplemental data from Google Trends, as a way to facilitate the machine learning model. Even though the final results were respectable compared to past research done on India and other countries, the model nevertheless has a limitation on the number of weeks ahead which the predictions are still considered to be good; with 7 weeks being the optimal result.

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A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles

arXiv

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arXiv 2024年

作者： Thakur, Nirmalya Su, Vanessa Shao, Mingchen Patel, Kesha A. Jeong, Hongseok Knieling, Victoria Bian, Andrew Department of Computer Science Emory University AtlantaGA30322 United States Department of Mathematics Emory University AtlantaGA30322 United States Program in Linguistics Emory University AtlantaGA30322 United States Goizueta Business School Emory University AtlantaGA30322 United States

Since the beginning of 2024, several countries have been experiencing an outbreak of measles. In the modern-day Internet of Everything lifestyle, social media platforms such as YouTube and TikTok have gained widespread popularity on a global scale due to their ability to facilitate the easy creation and dissemination of videos. During virus outbreaks of the recent past, videos on social media platforms played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result in the last few years, researchers from different disciplines have focused on the development of datasets of videos published on YouTube, TikTok, and similar websites. No prior work in this field has focused on the development of a dataset of videos about the ongoing outbreak of measles, published on social media platforms. The work of this paper aims to address this research gap and presents a dataset that contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024, available at https://***/10.21227/40s8-xf63. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. h

关键词： Video analysis

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Compression for qubit clocks

arXiv

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arXiv 2022年

作者： Yang, Yuxiang Chiribella, Giulio Hayashi, Masahito Department of Computer Science The University of Hong Kong Hong Kong Department of Computer Science The University of Oxford United Kingdom Canadian Institute for Advanced Research CIFAR Program in Quantum Information Science Canada Graduate School of Mathematics Nagoya University Japan Centre for Quantum Technologies National University of Singapore Singapore

Two-level (qubit) clock systems are often used to perform precise measurement of time. In this work, we propose a compression protocol for n identically prepared states of qubit clocks. The protocol faithfully encodes the states into (1/2) log n qubits and (1/2) log n classical bits and works even in the presence of noise. If the purity of the clock states is fixed, (1/2) log n qubits are sufficient. We also prove that this protocol requires the minimum amount of total memory among all protocols with vanishing error in the large n limit. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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Innovative Region Convolutional Neural Network Algorithm for Object Identification

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第4期8卷

作者： Permanasari, Yurika Ruchjana, Budi Nurani Hadi, Setiawan Rejito, Juli Doctor Program of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Bandung 40132 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Bandung 40132 Indonesia Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Bandung 40132 Indonesia

Object identification is a part of the field of computer science, namely, image processing, whose research continues to innovate. Object identification describes an object based on the main characteristics of the object. Many research innovations related to object identification have been carried out to obtain optimal identification results. The convolutional neural network (CNN) is one of the algorithms that is widely used by researchers in the field of object identification or object recognition in digital images. The purpose of this study was to analyze the development of object identification in the search for the best algorithm in terms of the speed and efficiency of identification. The article data used were obtained from several sources, namely, Dimensions AI, science Direct, and Google Scholar. The database search results obtained 1041 articles in the form of publications from 2010–2021. Through a systematic literature review based on the articles obtained, 32 articles were selected. The evaluation of the articles was carried out in the form of article data visualization, object identification algorithm development, and the research objects used. CNN’s research innovation is growing rapidly, with improvements being made to the identification techniques in its algorithmic architecture. The use of the CNN algorithm in the identification of image objects, starting with the region CNN technique, is improved with Fast R-CNN, Faster-CNN, and Mask R-CNN. The object of research has developed from facial recognition and the identification of moving images to the introduction of ancient manuscripts that are useful for the development of history and tourism. The successful identification of ancient scripted texts will greatly assist the availability of such manuscripts in a digital format, which allows for further multidisciplinary research. The availability of ancient manuscripts in a digital format also helps the government to preserve culture and increase people’s u

关键词： ancient manuscripts Fast R-CNN Faster R-CNN Mask R-CNN object identification R-CNN

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Utilizing probabilistic entanglement between sensors in quantum networks

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Physical Review Applied 2024年第6期22卷 064085-064085页

作者： Emily A. Van Milligen Christos N. Gagatsos Eneet Kaur Don Towsley Saikat Guha Department of Physics The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Program in Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Cisco Quantum Lab Los Angeles USA College of Computer Science University of Massachusetts Amherst Massachusetts USA Department of Electrical and Computer Engineering The University of Maryland College Park Maryland 20742 USA

One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.

关键词： Quantum entanglement

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Deep learning performance in sentiment analysis 4

Deep learning performance in sentiment analysis

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4th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2020

作者： Manalu, Boy Utomo Tulus Efendi, Syahril Graduate Program of Computer Science Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Department of Mathematics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia

ISBN: (纸本)9781728188706

A problem of sentiment analysis is the text representation, that encodes text into a continuous vector by arranging projections from semantics to the points in high dimensional space. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis requires a good method of text representation to be used as an embedding layer. In this study, we analyzed deep learning with the Recurrent Neural Network (RNN) method with Long Short-Term Memory (LSTM) variants in sentiment classification. We compare the performance of the LSTM network with Word2Vec to the network without Word2Vec as word embedding. Sentiment data used is derived from user-provided reviews of the applications in Google Play. The training process involves the Dropout layer and Early Stopping points to prevent overfitting. The results showed that the LSTM network using a word embedding Word2Vec is better than without Word2Vec. LSTM with Word2Vec 300 words dimension got a low error value of 0.3287 with an accuracy of 86.76%. While the LSTM testing results without Word2Vec get the lowest error of 0.3751 with an accuracy of 84.14%. © 2020 IEEE.

关键词： Long short-term memory

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SMOTE for Handling Imbalanced Data Problem : A Review 6

SMOTE for Handling Imbalanced Data Problem : A Review

引用

6th International Conference on Informatics and Computing, ICIC 2021

作者： Pradipta, Gede Angga Wardoyo, Retantyo Musdholifah, Aina Sanjaya, I Nyoman Hariyasa Ismail, Muhammad Universitas Gadjah Mada Faculty of Mathematics and Natural Science Doctoral Program Department of Computer Science and Electronics Yogyakarta Indonesia University/Sanglah General Hospital Faculty of Medicine Udayana Department of Obstetrics and Gynecology Bali Indonesia UIN Syarif Hidayatullah Faculty of Social and Political Sciences Jakarta Indonesia

ISBN: (纸本)9781665421553

Imbalanced class data distribution occurs when the number of examples representing one class is much lower than others. This conditioning affects the prediction accuracy degraded on minority data. To overcome this problem, Synthetic Minority Oversampling Technique (SMOTE) is a pioneer oversampling method in the research community for imbalanced classification. The basic idea of SMOTE is oversampled by creating a synthetic instance in feature space formed by the instance and its K-nearest neighbors due to the ability to avoid overfitting and assist the classifier in finding decision boundaries between classes. In this paper, we review current issue and problem occurs in classification with imbalanced data, performance evaluation in imbalanced data, a survey on an extension of SMOTE in recent years, and finally identify current challenges and future work in learning with imbalanced data. © 2021 IEEE.

关键词： Classification (of information)

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