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arXiv 2021年

作者： Chiu, Ching-Yu Ching, Joann Hsiao, Wen-Yi Chen, Yu-Hua Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan Research Center for IT Innovation Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Dept. CSIE National Cheng Kung University

Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years. In training such deep learning based models, data augmentation has been found an important technique. However, existing data augmentation methods for this task mainly target at balancing the distribution of the training data with respect to their tempo. In this paper, we investigate another approach for data augmentation, to account for the composition of the training data in terms of the percussive and non-percussive sound sources. Specifically, we propose to employ a blind drum separation model to segregate the drum and non-drum sounds from each training audio signal, filtering out training signals that are drumless, and then use the obtained drum and non-drum stems to augment the training data. We report experiments on four completely unseen test sets, validating the effectiveness of the proposed method, and accordingly the importance of drum sound composition in the training data for beat and downbeat tracking. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Predicting User Preferences with XGBoost Learning to Rank Method 3

Predicting User Preferences with XGBoost Learning to Rank Me...

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3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781728184067

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation. © 2020 IEEE.

关键词： Recommender systems

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User Preference Modelling from Movie Database 7

User Preference Modelling from Movie Database

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Film industry becomes one of the largest amongst the other economic sectors worldwide nowadays. This industry also has a significant impact in the global economic market. Many movies have been produced each year. However, some movies still fail to reach break-even points every year. Players in this industry need to think carefully when spending most of their money to make a great and successful movie in the market. Several attempts also need to be made to estimate the success performance before a movie can be released. On the audience side, there is an indicator that we can use to decide that a movie is good by looking to its ratings. The industry can use this rating systems as a factor to be considered when evaluating the past movie performances to make better movie for the next production. This study aims to predict the movie ratings and find the most important features which correlate to the ratings. We performed an experiment on IMDb dataset which contains 5,043 movie metadata on four models of machine learning, Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN). From the experiment, Random Forest achieves the best performance amongst the four models. © 2020 IEEE.

关键词： Decision trees

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Discover and align taxonomic context priors for open-world semi-supervised learning 23

Discover and align taxonomic context priors for open-world s...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yu Wang Zhun Zhong Pengchong Qiao Xuxin Cheng Xiawu Zheng Chang Liu Nicu Sebe Rongrong Ji Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China School of Computer Sceince University of Nottingham United Kingdom School of Electronic and Computer Engineering Peking University Shenzhen China and Department of Information Engineering and Computer Science University of Trento Italy School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China and Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Department of Automation Tsinghua University Beijing China Department of Information Engineering and Computer Science University of Trento Italy School of Electronic and Computer Engineering Peking University Shenzhen China and Peng Cheng Laboratory Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China

Open-world Semi-Supervised Learning (OSSL) is a realistic and challenging task, aiming to classify unlabeled samples from both seen and novel classes using partially labeled samples from the seen classes. Previous works typically explore the relationship of samples as priors on the pre-defined single-granularity labels to help novel class recognition. In fact, classes follow a taxonomy and samples can be classified at multiple levels of granularity, which contains more underlying relationships for supervision. We thus argue that learning with single-granularity labels results in sub-optimal representation learning and inaccurate pseudo labels, especially with unknown classes. In this paper, we take the initiative to explore and propose a uniformed framework, called Taxonomic context prIors Discovering and Aligning (TIDA), which exploits the relationship of samples under various granularity. It allows us to discover multi-granularity semantic concepts as taxonomic context priors (i.e., sub-class, target-class, and super-class), and then collaboratively leverage them to enhance representation learning and improve the quality of pseudo labels. Specifically, TIDA comprises two components: i) A taxonomic context discovery module that constructs a set of hierarchical prototypes in the latent space to discover the underlying taxonomic context priors; ii) A taxonomic context-based prediction alignment module that enforces consistency across hierarchical predictions to build the reliable relationship between classes among various granularity and provide additions supervision. We demonstrate that these two components are mutually beneficial for an effective OSSL framework, which is theoretically explained from the perspective of the EM algorithm. Extensive experiments on seven commonly used datasets show that TIDA can significantly improve the performance and achieve a new state of the art. The source codes are publicly available at https://***/rain305f/TIDA.

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Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs

Deep Learning Approach to Modeling and Exploring Random Sour...

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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan Center for mmWave Smart Radar System and Technologies National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

In this paper, for the first time, deep learning (DL) based artificial neural network (ANN) is applied to model the effects of various random variations: work function fluctuation, random dopant fluctuation, and interface trap fluctuation, on gate-all-around silicon nanosheet MOSFETs. The number of fluctuations for each source variation is used as input features and their effects on devices of interest are studied qualitatively and quantitatively. The key figures of merit (FoM) are also extracted accurately from the transfer characteristics, which shows the competency of the ANN model in the domain of device modeling. © 2022 IEEE.

关键词： Nanosheets

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Dynamic Digital Signage System: A Cost-Effective and Unified Web-Based Solution for Content and Analytics Management

Dynamic Digital Signage System: A Cost-Effective and Unified...

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Image Processing and Media computing (ICIPMC), International Conference on

作者： Kriselyn Cabading Orlando Malaca Ronaldo Juanatas Policarpio Tena Joselito Eduard Goh Marie Luvett Goh Irish Juanatas Roben Juanatas College of Engineering Technological University of the Philippines Manila Philippines College of Industrial Education Technological University of the Philippines Manila Philippines Graduate Program and External Studies Technological University of the Philippines Manila Philippines College of Information System De La Salle – College of Saint Benilde Manila Philippines IT Department College of Computer Studies and Multimedia Arts Far Eastern University – Institute of Technology Manila Philippines Information Technology Department Far Eastern University Quezon City Philippines College of Computing and Information Technology National University Philippines Manila Philippines

While several content management systems (CMS) and audience analytics tools are available for digital signage in the market, they are often sold separately and can be expensive. Therefore, this project aims to design a cost-effective and unified web-based solution for digital signage that combines content management and audience analytics functions, reducing the need for multiple purchases. This can be achieved by utilizing Raspberry Pi technology, known for its cost-effectiveness and versatility in integrations, along with a face recognition camera and machine learning methods. This proof of concept demonstrates the integration of these components to create a dynamic digital signage system. Overall, this project has the potential to offer an affordable solution for companies aiming to efficiently manage and optimize their digital marketing strategies, especially in the Digital Out-of-Home (DOOH) Advertising space.

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Learning Pairwise Preferences from Movie Ratings

Learning Pairwise Preferences from Movie Ratings

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International Conference on ICT For Smart Society (ICISS)

作者： Nunung Nurul Qomariyah Ahmad Nurul Fajar Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781665404228

ISBN: (纸本)9781665404235

Preference Elicitation is now considered a crucial stage in recommender system. That is the stage where we collect and query preferences of the users as part of interactive decision support system. Preference elicitation can be performed using many ways, such as asking the user explicitly to give ratings or feedbacks for some products, or implicitly observing their behaviour. Once this preference has been collected by the system, it can then be used to generate item recommendation. Using explicit feedback itself, such as rating only, has several drawbacks. Using ratings is very subjective. Two users with similar taste might give different rating to the same item. A user also may not be consistent in giving number to express their liking. In other case, a user may like an item more than the other items, but feels difficult to give lower rating on the other item, because it means that the particular item is just as bad as the one he/she does not like. By showing two different items as a pair, it will be easier for the users to pick which one is the best. In this paper, we show the result of our experiment in predicting user pairwise preferences by using their movie ratings data. We evaluated five different machine learning algorithms to predict the data in pairwise preference format. The result shows that K-Nearest Neighbour and Random Forest outperformed the other algorithms.

关键词： Motion pictures Predictive models Recommender systems Social networking (online) Industries Encoding Task analysis

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Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

arXiv

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arXiv 2025年

作者： Chang, Yen Jui Wang, Wei-Ting Liu, Chen-Yu Wang, Yun-Yuan Chang, Ching-Ray Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Department of Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate Institute of Applied Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan NVIDIA AI Technology Center NVIDIA Corp Taipei610101 Taiwan

We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations (0–9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation. © 2025, CC BY.

关键词： Qubits

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Decreased incidence followed by comeback of pediatric infections during the COVID‑19 pandemic in Japan

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World Journal of Pediatrics 2022年第8期18卷 564-567页

作者： Cyrus Ghaznavi Haruka Sakamoto Takayuki Kawashima Sayaka Horiuchi Masahiro Ishikane Sarah Krull Abe Daisuke Yoneoka Akifumi Eguchi Yuta Tanoue Masahiro Hashizume Shuhei Nomura Department of Health Policy and Management School of MedicineKeio University35 ShinanomachiShinjuku-kuTokyo 160-8582Japan Medical Education Program Washington University School of Medicine in St.LouisSaint LouisUSA Department of Global Health Policy Graduate School of MedicineThe University of TokyoTokyoJapan Tokyo Foundation for Policy Research TokyoJapan Department of Hygiene and Public Health Tokyo Women’s Medical UniversityTokyoJapan Department of Mathematical and Computing Science Tokyo Institute of TechnologyTokyoJapan Center for Birth Cohort Studies University of YamanashiYamanashiJapan Disease Control and Prevention Center National Center for Global Health and MedicineTokyoJapan Division of Prevention National Cancer Center Institute for Cancer ControlTokyoJapan Infectious Disease Surveillance Center National Institute of Infectious DiseasesTokyoJapan Department of Sustainable Health Science Center for Preventive Medical SciencesChiba UniversityChibaJapan Institute for Business and Finance Waseda UniversityTokyoJapan

During the coronavirus disease 2019(COVID-19)pandemic,the Japanese government implemented several infection control measures,such as states of emergency(SoE)and school closures,and encouraged universal masking and hand *** activity restrictions and improved hygiene were found to be associated with declines in the incidence of infuenza during the 2019–2020 winter season[1].Prior studies have found that the incidence of seven pediatric infections,such as pharyngoconjunctivitis and varicella,decreased during and after the school closures in Japan[2].As activity restrictions continue to be lifted,there is growing concern that pediatric infections may suddenly return given that the pool of susceptible children increased during the pandemic[3].In this study,we use national infectious disease sentinel data from January 2012 to October 2021 to assess changes in the reported number of pediatric infections in Japan during the COVID-19 *** hypothesize that the reporting of many infectious diseases decreased during the early stages of the pandemic but ultimately returned to normal when activity restrictions were lifted.

关键词： closure return suddenly

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