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arXiv 2020年

作者： Chen, Xinyun Liu, Yunan Hong, Guiyu School of Data Science Chinese University of Hong Kong Shenzhen China Dept. of Industrial and Systems Engineering North Carolina State University United States

We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queue, where the service provider's objective is to obtain the optimal service fee p and service capacity µ so as to maximize the cumulative expected profit (the service revenue minus the staffing cost and delay penalty). Due to the complex nature of the queueing dynamics, such a problem has no analytic solution so that previous research often resorts to heavy-traffic analysis where both the arrival rate and service rate are sent to infinity. In this work we propose an online learning framework designed for solving this problem which does not require the system's scale to increase. Our framework is dubbed Gradient-based Online Learning in Queue (GOLiQ). GOLiQ organizes the time horizon into successive operational cycles and prescribes an efficient procedure to obtain improved pricing and staffing policies in each cycle using data collected in previous cycles. data here include the number of customer arrivals, waiting times, and the server's busy times. The ingenuity of this approach lies in its online nature, which allows the service provider do better by interacting with the environment. Effectiveness of GOLiQ is substantiated by (i) theoretical results including the algorithm convergence and regret analysis (with a logarithmic regret bound), and (ii) engineering confirmation via simulation experiments of a variety of representative GI/GI/1 queues. Copyright © 2020, The Authors. All rights reserved.

关键词： Costs

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Air quality data prediction using data analytics

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AIP Conference Proceedings 2024年第1期2742卷

作者： Senthil Kumar Sidharthan Kalaiarasan Nancy Anvar Shathik Kavietha Dept. of Computer Science and Egg Chennai Institute of Technology Chennai Tamilnadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and technical sciences(Deemed to be university) Chennai India Department of Cloud Technology & Data Science iNurture Education Solutions Srinivas University College of Engineering & Technology Bangalore India. Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India.

Air pollution is usually described as the release into the atmosphere of contaminants which are harmful to human health and the environment. It may be described as one of the most serious risks human beings have ever faced because to the growing more catastrophic problem of air pollution in the globe. A description is provided of how our air quality sensor system is designed, implemented and optimised, which provides a live and fine grain air quality map of the area under surveillance. The average joint failure in the generated real-time air quality map should be maintained to a minimal when unmeasured data values as part of the objective are deducted. In terms of studying the quality issues, Big data Technology has been recommended to be utilised in order to organise the air quality sensing equipment sensing tasks appropriately. In addition, the prediction of air quality based on climatic conditions is explored using various machine learning techniques for the data given.

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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

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Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

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On the unification of rational terms

Computer Software

引用

Computer Software 2018年第4期35卷 151-163页

作者： Iwami, Munehiro Dept. of Information Systems Design and Data Science Interdisciplinary Faculty of Science and Engineering Shimane University Japan

First-order unification algorithms on finite terms are formalized by inference rules, and their termination, soundness and completeness have been presented in many papers. Unification algorithms on rational terms have also been presented in several papers. However, it has not been shown that the solution of the set obtained by inference rules from a given set of equations coincides with the rational substitution which is the most general unifier of the original set. Furthermore, the precise proofs of termination, soundness and completeness are not clear in many cases. In this paper, we revisit the foundations of unification algorithm on rational terms within the framework of term rewriting systems. First, we show that the solution of a regular system is the most general unifier of the set of equations corresponding to the regular system. Next, we reformulate the unification algorithm on rational terms by inference rules, and then give the proofs of its termination, soundness and completeness, respectively. Finally, we show that a unifiable set of equations has a most general unifier as a rational substitution. © 2018 IEEE Computer Society. All Rights Reserved.

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Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

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SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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madmpy: A Python library for creating and validating data Management Plans

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SoftwareX 2025年 31卷

作者： Alberto Ballesteros-Rodríguez Miguel-Ángel Sicilia Elena García-Barriocanal Biomedical Data Science and Engineering Research Group Ramón y Cajal University Hospital and Ramón y Cajal Health Research Institute (IRYCIS) 28034 Madrid Spain Universidad de Alcalá Computer Science dept. Polytechnic Building Ctra. Barcelona km. 33.6 28871 Alcalá de Henares (Madrid) Spain

data Management Plans (DMPs) are documents that describe the data used and produced during the course of research projects. Machine-actionable DMPs (maDMPs) are plans written in computer-readable formats. They are designed to support the automation of data-generation processes in scientific research. The madmpy Python package validates maDMPs that follow any version of the RDA DMP Common Standard. These plans can be written in JSON format or built programmatically. It also supports institution- or domain-specific extensions and additional validations that adhere to the standard. The library serves as a building block for research data engineering workflows. It promotes data management and accountability through the use of structured DMPs.

关键词： data Management Plan Machine-actionable data validation FAIR

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Generative pre-training for paraphrase generation by representing and predicting spans in exemplars

arXiv

引用

arXiv 2020年

作者： Bui, Tien-Cuong Le, Van-Duc To, Hai-Thien Cha, Sang Kyun Dept. Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Graduate School of Data Science Seoul National University Seoul Korea Republic of

Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems. Despite some encouraging results, recent methods either confront the problem of favoring generic utterance or need to retrain the model from scratch for each new dataset. This paper presents a novel approach to paraphrasing sentences, extended from the GPT-2 model. We develop a template masking technique, named first-order masking, to masked out irrelevant words in exemplars utilizing POS taggers. So that, the paraphrasing task is changed to predicting spans in masked templates. Our proposed approach outperforms competitive baselines, especially in the semantic preservation aspect. To prevent the model from being biased towards a given template, we introduce a technique, referred to as second-order masking, which utilizes Bernoulli distribution to control the visibility of the first-order-masked template’s tokens. Moreover, this technique allows the model to provide various paraphrased sentences in testing by adjusting the second-order-masking level. For scale-up objectives, we compare the performance of two alternatives template-selection methods, which shows that they were equivalent in preserving semantic information. © 2020, CC-BY-NC-SA.

关键词： Computational linguistics

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Interactive concept mining on personal data – Bootstrapping semantic services

arXiv

引用

arXiv 2019年

作者： Schröder, Markus Jilek, Christian Dengel, Andreas Smart Data & Knowledge Services Dept. DFKI GmbH Kaiserslautern Germany Computer Science Dept. TU Kaiserslautern Germany

Semantic services (e.g. Semantic Desktops) are still afflicted by a cold start problem: in the beginning, the user’s personal information sphere, i.e. files, mails, bookmarks, etc., is not represented by the system. Information extraction tools used to kick-start the system typically create 1:1 representations of the different information items. Higher level concepts, for example found in file names, mail subjects or in the content body of these items, are not extracted. Leaving these concepts out may lead to underperformance, having to many of them (e.g. by making every found term a concept) will clutter the arising knowledge graph with non-helpful relations. In this paper, we present an interactive concept mining approach proposing concept candidates gathered by exploiting given schemata of usual personal information management applications and analysing the personal information sphere using various metrics. To heed the subjective view of the user, a graphical user interface allows to easily rank and give feedback on proposed concept candidates, thus keeping only those actually considered relevant. A prototypical implementation demonstrates major steps of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Terminology

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Deep reinforcement learning for iot networks: age of information and energy cost tradeoff

arXiv

引用

arXiv 2020年

作者： Wu, Xiongwei Li, Xiuhua Li, Jun Ching, P.C. Poor, H. Vincent Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong Dept. of Electrical Engineering Princeton University Princeton United States School of Big Data & Software Engineering Chongqing University Chongqing China School of Electronic & Optical Engineering Nanjing University of Science and Technology Nanjing China

In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for data consumers. However, a critical issue of IoT sensing is that data are usually transient, which necessitates temporal updates of caching content items while frequent cache updates could lead to considerable energy cost and challenge the lifetime of IoT sensors. To address this issue, we adopt the Age of Information (AoI) to quantify data freshness and propose an online cache update scheme to obtain an effective tradeoff between the average AoI and energy cost. Specifically, we first develop a characterization of transmission energy consumption at IoT sensors by incorporating a successful transmission condition. Then, we model cache updating as a Markov decision process to minimize average weighted cost with judicious definitions of state, action, and reward. Since user preference towards content items is usually unknown and often temporally evolving, we therefore develop a deep reinforcement learning (DRL) algorithm to enable intelligent cache updates. Through trial-and-error explorations, an effective caching policy can be learned without requiring exact knowledge of content popularity. Simulation results demonstrate the superiority of the proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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引用

2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Li, Xiuhua Wang, Xiaofei Sheng, Zhengguo Hu, Chunqiang Leung, Victor C.M. School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science and Technology Tianjin University Tianjin China Dept. of Engineering and Design University of Sussex Falmer Brighton United Kingdom Dept. Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (纸本)9781450371582

Content requests from mobile users are explosively increasing due to the growing popularity of mobile social activities. However, current mobile network infrastructure cannot effectively support the generated massive mobile data traffic while maintaining consistent and acceptable network performances. To deal with such a serious challenge, caching popular content at the edges of mobile networks has been recognized as a promising solution. In this paper, we introduce the content caching technique in Fog-Cloud based radio access networks for offering joint Fog-enabled and Cloud-enabled data services. Then we propose a collaborative hierarchical Fog-Cloud caching framework based on predicting content popularity with the Auto Regressive model. Our objective is to explore the maximum capacity of the considered network infrastructure by effectively offloading network traffic. Trace-driven evaluations demonstrate the effectiveness of our proposed caching mechanism in terms of traffic offload. © 2019 Association for Computing Machinery.

关键词： Fog

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