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5th Brazilian Technology Symposium, BTSym 2019

作者： Collucci, Daniel Junqueira, Alexandre Forster, Carlos Itaú-Unibanco Data Science Team São Paulo Brazil Itaú-Unibanco Advanced Analytics Team São Paulo Brazil Computer Science Department Aerospace Technology Institute São Paulo Brazil

ISBN: (纸本)9783030575472

YELP reviews strongly influence the business’s reputation and revenue as well as customers and new-users attitudes toward the acquisition of products and services. Due to the overwhelming number of customer reviews available on Web sites, usually, the decision-making process of customers, new-users, and business owners, is not associated with the daunting task of reading reviews online. Text mining can address the issue of summarizing huge volumes of unstructured data;furthermore, text mining can extract emotions, sentiments, and insights from texts written by customers. This study proposes a text mining approach to unravel suggestions from YELP reviews. This study shows that suggestion mining differentiates reviews with suggestions from reviews without suggestions and therefore can identify new services and new product ideas from customer reviews. © 2021, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering

arXiv

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arXiv 2025年

作者： Muto, Yui Zielewski, Michael R. Shinozaki, Motoya Noro, Kosuke Otsuka, Tomohiro Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Department of Electronic Engineering Graduate School of Engineering Tohoku University Aoba 6-6-05 Aramaki Aoba-Ku Sendai980-8579 Japan Graduate School of Information Sciences Tohoku University 6-3-09 Aramaki-aza-Aoba Aoba-ku Sendai980-8579 Japan Unprecedented-scale Data Analytics Center Tohoku University 468-1 Aramaki-aza-Aoba Aoba-ku Sendai980-8572 Japan WPI Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Science and Innovation in Spintronics Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan

To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Hough transforms

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Oscillation resolution for massive cell phone traffic data 1

Oscillation resolution for massive cell phone traffic data

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1st Workshop on Mobile data, Mobidata 2016, co-located with MobiSys 2016

作者： Qi, Ling Qiao, Yuanyuan Abdesslem, Fehmi Ben Ma, Zhanyu Yang, Jie Center for Data Science Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network BUPT Beijing China Decisions Networks and Analytics Laboratory SICS Stockholm Sweden Center for Data Science Pattern Recognition and Intelligent System Lab BUPT Beijing China

ISBN: (纸本)9781450343275

Cellular towers capture logs of mobile subscribers whenever their devices connect to the network. When the logs show data traffic at a cell tower generated by a device, it reveals that this device is close to the tower. The logs can then be used to trace the locations of mobile subscribers for different applications, such as studying customer behaviour, improving location-based services, or helping urban planning. However, the logs often suffer from an oscillation phenomenon. Oscillations may happen when a device, even when not moving, does not only connect to the nearest cell tower, but is instead unpredictably switching between multiple cell towers because of random noise, load balancing, or simply dynamic changes in signal strength. Detecting and removing oscillations are a challenge when analyzing location data collected from the cellular network. In this paper, we propose an algorithm called SOL (Stable, Oscillation, Leap periods) aimed at discovering and reducing oscillations in the collected logs. We apply our algorithm on real datasets which contain about 18.9 TB of traffic logs generated by more than 3 million mobile subscribers covering about 21000 cell towers and collected during 27 days from both GSM and UMTS networks in northern China. Experimental results demonstrate the ability and effectiveness of SOL to reduce oscillations in cellular network logs. © 2016 ACM.

关键词： Location based services

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VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification 9

VQC-COVID-NET: Vector Quantization Contrastive Learning for ...

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9th NAFOSTED Conference on Information and Computer science, NICS 2022

作者： Trinh, Linh Ha, Bach Tran, Anh Tu Vingroup Advanced Analytics Data Science Department Hanoi Viet Nam Hanoi University of Science and Technology Falculty of Applied Mathematics and Informatics Hanoi Viet Nam Academy of Cryptography Techniques Falculty of Information Security Hanoi Viet Nam

ISBN: (纸本)9781665454223

Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://***/khaclinh/VQC-COVID-NET. © 2022 IEEE.

关键词： COVID-19

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DTOR: Decision Tree Outlier Regressor to explain anomalies

arXiv

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arXiv 2024年

作者： Crupi, Riccardo Regoli, Daniele Sabatino, Alessandro Damiano Marano, Immacolata Brinis, Massimiliano Albertazzi, Luca Cirillo, Andrea Cosentini, Andrea Claudio Data Science & Artificial Intelligence Intesa Sanpaolo Italy Audit Data & Advanced Analytics Intesa Sanpaolo Italy

Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively perform actionable counteracts. The ever more widespread use of sophisticated Machine Learning approach to identify anomalies make such explanations more challenging. We present the Decision Tree Outlier Regressor (DTOR), a technique for producing rule-based explanations for individual data points by estimating anomaly scores generated by an anomaly detection model. This is accomplished by first applying a Decision Tree Regressor, which computes the estimation score, and then extracting the relative path associated with the data point score. Our results demonstrate the robustness of DTOR even in datasets with a large number of features. Additionally, in contrast to other rule-based approaches, the generated rules are consistently satisfied by the points to be explained. Furthermore, our evaluation metrics indicate comparable performance to Anchors in outlier explanation tasks, with reduced execution time. © 2024, CC BY.

关键词： Decision trees

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Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexistence Equilibria

Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexis...

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IEEE Conference on Decision and Control

作者： Vishwaraj Doshi Jie Hu Do Young Eun Data Science and Advanced Analytics Team at IQVIA Department of Electrical and Computer Engineering NCSU

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a system in which two viruses of the Susceptible-Infected-Susceptible (SIS) type compete over general, overlaid graphs. While such systems have been the focus of many recent works, they have mostly been studied in the sense of convergence analysis, with no existing results quantifying the non-trivial coexistence equilibria (CE) - that is, when both competing viruses maintain long term presence over the network. In this paper, we prove monotonicity of the CE with respect to effective infection rates of the two viruses, and provide the first quantitative analysis of such equilibria in the form of upper bounds involving spectral radii of the underlying graphs, as well as positive equilibria of related single-virus systems. Our results provide deeper insight into how the long term infection probabilities are affected by system parameters, which we further highlight via numerical results.

关键词： Epidemics Analytical models Upper bound Statistical analysis Stars Topology Numerical models

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Towards a Transportable Causal Network Model Based on Observational Healthcare data 2

Towards a Transportable Causal Network Model Based on Observ...

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2nd AIxIA Workshop on Artificial Intelligence for Healthcare, HC@AIxIA 2023

作者： Bernasconi, Alice Zanga, Alessio Lucas, Peter J.F. Scutari, Marco Stella, Fabio University of Milano - Bicocca Milan Italy Evaluative Epidemiology Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland University of Twente Enschede Netherlands Lugano Switzerland

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods. © 2023 CEUR-WS. All rights reserved.

关键词： Risk assessment

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Magnetic hydrophobic deep eutectic solvent-based microextraction for rapid and simultaneous detection of sulfonamide and estrogen residues in milk using high-performance liquid chromatography-diode array detection

引用

Microchemical Journal 2025年 214卷

作者： Nor Munira Hashim Mohd Yusmaidie Aziz Noorfatimah Yahaya Kartika A. Madurani Hikmat Waleed Alahmad Nur Nadhirah Mohamad Zain Department of Toxicology Advanced Medical and Dental Institute (AMDI) Universiti Sains Malaysia Bertam 13200 Kepala Batas Penang Malaysia Laboratory of Instrumentation and Analytical Sciences Chemistry Department Faculty of Science and Data Analytics Institute Teknologi Sepuluh Nopember Arief Rahman Hakim Surabaya 60111 Indonesia Research Centre for Chemistry National Research and Innovation Agency (BRIN) Tangerang Selatan 15314 Indonesia Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

Milk, a vital source of nutrition and bone health, is increasingly at risk of contamination by veterinary pharmaceuticals such as sulfonamides and estrogens, posing significant public health and environmental concerns. Although hydrophobic deep eutectic solvents (HDES) have been explored in analytical applications, previous studies primarily focused on HDES development without incorporating magnetic properties of sorbent design. In this study, we introduce a novel magnetic sorbent (Fe 3 O 4 @HDES), synthesized by functionalising magnetic nanoparticles with an HDES composed of decanoic acid and 2-pentanol. This is the first report on the application of such a sorbent in magnetic dispersive micro-solid phase extraction (D-µ-SPE) for the simultaneous determination of sulfonamide and estrogen residues in milk. Coupled with high-performance liquid chromatography-diode array detection (HPLC-DAD), the method achieved excellent performance, including low detection limit (0.07–0.64 µg kg −1 ), good linearity (R 2 : 0.992–0.998), and high recoveries (87.3–115.1 %) with good precision (RSD: 0.2–11.3 %). Method optimisation using the Taguchi orthogonal array significantly enhanced extraction efficiency while reducing solvent consumption. The Fe 3 O 4 @HDES sorbent provided dual interaction capabilities—hydrophobic and hydrogen bonding—improving selectivity, phase separation, and reusability compared to unmodified Fe 3 O 4 . The method’s sustainability was comprehensively validated using four green chemistry assessment tools: Analytical Eco-Scale, ComplexGAPI, AGREEprep, and Blue Applicability Grade Index (BAGI) . This innovative approach establishes the first environmentally robust HDES-based sorbent for magnetic D-µ-SPE, offering a rapid, sensitive, and green strategy for pharmaceutical residue monitoring in dairy products.

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Mesoporous aluminosilicate from metakaolin with natural surfactants for waste cooking oil conversion to hydrocarbon fuels via low-temperature pyrolysis

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Nadhifah, Nada Jovita, Stella Subagyo, Riki Utami, Diana Inas Agustina, Khawiyatur Riv'ah Holilah, Holilah Asikin-Mijan, Nurul Bahruji, Hasliza Nugraha, Reva Edra Jalil, Aishah Abdul Suprapto, Suprapto Prasetyoko, Didik Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Sukolilo Surabaya 60111 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN Cibinong 16911 Indonesia Department of Chemical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia UKM Bangi Selangor Darul Ehsan 43600 Malaysia Centre for Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE1410 Brunei Darussalam Department of Chemical Engineering Faculty of Engineering Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya 60294 Indonesia Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia UTM Johor Johor Bahru 81310 Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia UTM Johor Johor Bahru 81310 Malaysia

The conversion of waste cooking oil to biofuel has been identified as a sustainable route to address energy issues. However, efficient conversion requires advanced catalysts to address challenges such as high acidity and thermal degradation. Sustainable catalyst production relying on naturally occurring materials reduced the overall carbon footprint and operational cost. This study investigates the transformation of kaolin minerals via metakaolinization into highly uniform mesoporous aluminosilicate nanoparticles when employing natural biosurfactants. The saponin extract from the Sapindus rarak plant was employed as a template to control morphology and mesoporosity. Synthesis of mesoporous aluminosilicate catalysts was conducted via sol-gel method to achieve uniform particle sizes (20–40 nm), with a pore volume of 0.55 cm³/g and a surface area of 197.29 m²/g. The catalyst demonstrated high performance with up to ∼99 % WCO conversion and 70 % hydrocarbon selectivity for C11–C14 and C15–C17. © 2025 The Authors

关键词： Kaolin Mesoporous silica Natural template Sapindus rarak Waste cooking oil

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Keeping Up With the Winner! Targeted Advertisement to Communities in Social Networks

arXiv

引用

arXiv 2024年

作者： Mallick, Shailaja Doshi, Vishwaraj Eun, Do Young North Carolina State University United States Data Science & Advanced Analytics IQVIA Inc

When a new product enters a market already dominated by an existing product, will it survive along with this dominant product? Most of the existing works have shown the coexistence of two competing products spreading/being adopted on overlaid graphs with same set of users. However, when it comes to the survival of a weaker product on the same graph, it has been established that the stronger one dominates the market and wipes out the other. This paper makes a step towards narrowing this gap so that a new/weaker product can also survive along with its competitor with a positive market share. Specifically, we identify a locally optimal set of users to induce a community that is targeted with advertisement by the product launching company under a given budget constraint. To this end, we model the system as competing Susceptible-Infected-Susceptible (SIS) epidemics and employ perturbation techniques to quantify and attain a positive market share in a cost-efficient manner. Our extensive simulation results with real-world graph dataset show that with our choice of target users, a new product can establish itself with positive market share, which otherwise would be dominated and eventually wiped out of the competitive market under the same budget constraint. © 2024, CC BY.

关键词： Competition

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