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9th Conference on Algebra and Coalgebra in computer science, CALCO 2021

作者： Adámek, Jiří Rosický, Jiří Department of Mathematics Czech Technical University Prague Czech Republic Institute of Theoretical Computer Science Technische Universität Braunschweig Germany Department of Mathematics and Statistics Faculty of Sciences Masaryk University Brno Czech Republic

ISBN: (纸本)9783959772129

Categories equivalent to single-sorted varieties of finitary algebras were characterized in the famous dissertation of Lawvere. We present a new proof of a slightly sharpened version: those are precisely the categories with kernel pairs and reflexive coequalizers having an abstractly finite, effective strong generator. A completely analogous result is proved for varieties of many-sorted algebras provided that there are only finitely many sorts. In case of infinitely many sorts a slightly weaker result is presented: instead of being abstractly finite, the generator is required to consist of finitely presentable objects. © Jiří Adámek and Jiří Rosický.

关键词： Algebra

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Development of a Method and System for Recycling Polystyrene Production Waste

Development of a Method and System for Recycling Polystyrene...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Rustam R. Farahov Rustam A. Burnashev Adelya I. Enikeeva Marina V. Bolsunovskaya Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

Currently, the consumption of natural resources is important for humanity. Therefore, reducing the production of production waste not only saves natural resources, but also increases the profit of the manufacturer. When producing polystyrene powder, the waste produced is a coarse powder with a wide range of particle sizes and irregular geometry. It is possible to obtain low-grade products from waste, such as disposable tableware, industrial containers, Petri dishes, etc. Manufacturers are interested in developing new processing methods to use waste materials to produce high-quality products. The purpose of the work is to study the possibility of using polystyrene production waste in selective laser sintering processes. The method we are developing for processing polystyrene-based polymer powder allows us to avoid the use of additional chemical exposure, and to select the optimal operating mode for a selective laser sintering installation with the resulting processed material. It is planned to introduce this method into the field of materials processing for additive manufacturing.

关键词： Natural resources Waste materials Powders Sintering Laser sintering Production Three-dimensional printing

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Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories

arXiv

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arXiv 2024年

作者： Olivo, Vicente Amado Kerzendorf, Wolfgang Cherinka, Brian Shields, Joshua V. Didier, Annie von der Wense, Katharina Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Space Telescope Science Institute BaltimoreMD21218 United States Outrider GoldenCO80403 United States Department of Computer Science University of Colorado Boulder BoulderCO80309 United States Institute of Computer Science Johannes Gutenberg University Mainz Mainz55128 Germany

Large scientific institutions, such as the Space Telescope science institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain-text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain-text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using Term Frequency-Inverse Document Frequency and classified with a Support Vector Machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies usage of MAST hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework’s interpretability makes it adaptable for use across observatories and other scientific facilities worldwide. Copyright © 2024, The Authors. All rights reserved.

关键词： Space telescopes

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Rank-Normalization, Folding, and Localization: An Improved (Formula presented) for Assessing Convergence of MCMC (with Discussion)*†

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Bayesian Analysis 2021年第2期16卷 667-718页

作者： Vehtarh, Aki Gelman, Andrew Simpson, Daniel Carpenter, Bob Burkner, Paul-Christian Department of Computer Science Aalto University Finland Department of Statistics Columbia University New York Department of Statistical Sciences University of Toronto Canada Center for Computational Mathematics Flatiron Institute New York

Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic R of Gelman and Rubin (1992) has serious flaws. Traditional R will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alternative rank-based diagnostic that fixes these problems. We also introduce a collection of quantile-based local efficiency measures, along with a practical approach for computing Monte Carlo error estimates for quantiles. We suggest that common trace plots should be replaced with rank plots from multiple chains. Finally, we give recommendations for how these methods should be used in practice. © 2021 International Society for Bayesian Analysis. All Rights Reserved.

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Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction

arXiv

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arXiv 2024年

作者： Xu, Ziyang Peng, Keqin Ding, Liang Tao, Dacheng Lu, Xiliang Institute of Artificial Intelligence School of Computer Science Wuhan University China School of Mathematics and Statistics Wuhan University China Hubei Key Laboratory of Computational Science Wuhan University China Beihang University China The University of Sydney Australia Nanyang Technological University Singapore

Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labels. However, the extent and impact of prompt bias within the model remain underexplored. In response, this paper quantifies the bias with various types of prompts and assesses their impact on different benchmarks. We show that: 1) all prompts in the experiments exhibit non-negligible bias, with gradient-based prompts like AutoPrompt and OptiPrompt displaying significantly higher levels of bias;2) prompt bias can amplify benchmark accuracy unreasonably by overfitting the test datasets, especially on imbalanced datasets like LAMA. Based on these findings, we propose a representation-based approach to mitigate the prompt bias during inference time. Specifically, we first estimate the biased representation using prompt-only querying, and then remove it from the model’s internal representations to generate the debiased representations, which are used to produce the final debiased outputs. Experiments across various prompts, PLMs, and benchmarks show that our approach can not only correct the overfitted performance caused by prompt bias, but also significantly improve the prompt retrieval capability (up to 10% absolute performance gain). Our findings shed new light on the underlying predicting mechanisms of prompt-based queries in PLMs. Hopefully, our plug-and-play approach can be a golden standard to strengthen PLMs toward reliable knowledge bases. Code and data are released in https://***/FelliYang/PromptBias. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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Analyzing Recurrence Patterns using Normalized Difference Vegetation Index (NDVI) of Man-Made Forests in Balaoan, La Union 8

Analyzing Recurrence Patterns using Normalized Difference Ve...

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Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII 2024

作者： Elmaguin, John Carlo Bulan, Jejomar Cadondon, Jumar Cabaluna, Mark Lester Roque, Floro Junior Jao, Jazzie Vallar, Edgar University of the City of Manila Manila1002 Philippines National Institute of Physics University of the Philippines-Diliman Quezon City1101 Philippines Department of Physics College of Science De La Salle University 0922 Philippines Department of Software Technology College of Computer Studies De La Salle University 0922 Philippines Division of Physical Sciences and Mathematics College of Arts and Sciences University of the Philippines Visayas Miagao Iloilo5023 Philippines

ISBN: (数字)9781510682757

ISBN: (纸本)9781510682740

Normalized Difference Vegetation Index (NDVI) is one of the common vegetation indices used to assess forest health. This includes the use of several wavelengths in the electromagnetic spectrum. Time Series analysis was the common process used in studying the temporal behavior of this remote sensing data. This process provides straightforward information about how NDVI changes over time and only provides minimal information. In this study, Recurrence Quantification Analysis (RQA) provides another approach to studying NDVI. Recurrence quantification analysis quantifies the number of recurrences of a state or data point in the time series using a Recurrence plot as a graphical representation. Based on the patterns of the recurrence plots, the NDVI on sites 1-5 are all deterministic periodic. The datasets manifest long diagonal lines, a distinctive feature of a periodic behavior. This Recurrence Quantification Analysis method can assess other biogeophysical parameters that exhibit periodic behavior that depends on different weather parameters. © 2024 SPIE.

关键词： Time series

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Language model CNN-driven similarity matching and classification for HTML-embedded product data 1

Language model CNN-driven similarity matching and classifica...

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1st Semantic Web Challenge on Mining the Web of HTML-Embedded Product Data, MWPD 2020

作者： Borst, Janos Krner, Erik Opasjumruskit, Kobkaew Niekler, Andreas Leipzig University Faculty of Mathematics and Computer Science Institute of Computer Science Augustusplatz 10 Leipzig04109 Germany Institute of Data Science Mlzerstrae 3 Jena07745 Germany

The Semantic Web Challenge Mining the Web of HTML- embedded Product Data aims to benchmark current technologies on the data integration tasks (1) product matching and (2) product classification, as recent years have seen significant use of semantic annotations in the e-commerce domain, but often with inconsistencies, no complete coverage or con icting information. We introduce a transformer-based approach for textual product matching and extend it with an CNN for product *** compare the in uence of different input feature combinations against prediction performance and introduce a technique to augment the classification task with additional information. We are able to outperform baseline results using text-only approaches. Copyright © 2020 for this paper by its authors.

关键词： Computational linguistics

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SEES-QL: An Improved Scalable and Energy-Efficient Scheme for WSNs based on Lightweight Q-learning

SEES-QL: An Improved Scalable and Energy-Efficient Scheme fo...

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Advances in science and Engineering Technology International Conferences (ASET)

作者： Antar S. H. Abdul-Qawy Ali Idarous Adnan Mohammed Sultan Mohammed Narendra Khatri Amir Shikhli Anwar Jarndal Department of Mathematics and Computer Science Abdulrahman Al-Sumait University Zanzibar Tanzania Department of Computer Engineering Hodeidah University Al Hudaydah Yemen Department of Computer Science and Information Technology The State University of Zanzibar Zanzibar Tanzania Department of Electronic and Computer Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Sustainable Engineering Asset Management Research Group University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

The energy efficiency of battery-operated sensing devices in IoT is a critical research area that needs further exploration. This paper employs lightweight reinforcement learning to improve energy savings in large-scale heterogeneous WSNs. We introduce SEES-QL (a Scalable and Energy-Efficient Scheme based on Q-Learning), an enhanced version of the zonal SEES protocol, that addresses the issue of frequent data transmission by dynamically adjusting nodes' transmission cycles without the need for a predefined model. In SEES-QL, on/off periods of radio transceivers are regulated based on transmission history and reading importance of each node independently, positively affecting total energy consumption, traffic load, and overall system lifetime. Performance evaluation demonstrates that SEES-QL achieves significant advancements in energy savings and transmission count reduction, leading to a remarkable 41% increase in the overall system lifetime compared to the traditional SEES protocol.

关键词： Performance evaluation Wireless sensor networks Protocols Q-learning Radio transceivers Energy conservation Telecommunication traffic Energy efficiency Sensors Load modeling

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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Chinese Automation Congress (CAC)

作者： Haozhe Zhang Liyan Ma Shihui Ying Institute of Artificial Intelligence Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Mathematics School of Science Shanghai University Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects.

关键词： Measurement Solid modeling Three-dimensional displays Active learning Object detection Reinforcement learning Entropy Real-time systems Labeling Autonomous vehicles

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Investigating Students’ Perception towards the Use of Social Media for Computing Education in Nigeria

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Journal of Information Systems Education 2021年第3期32卷 212-228页

作者： Agbo, Friday J. Olawumi, Olayemi Balogun, Oluwafemi S. Sanusi, Ismaila T. Olaleye, Sunday A. Sunday, Kissinger Kolog, Emmanuel A. Atsa’am, Donald D. Adusei-Mensah, Frank Adegbite, Ayobami Ipeayeda, Funmilola W. School of Computing University of Eastern Finland 80110 Joensuu 70211 Kuopio Finland Department of Marketing Management and International Business University of Oulu Oulu90570 Finland Department of Computer Science National Chengchi University Taipei Taiwan Department of Operations and MIS University of Ghana Business School Accra Ghana Department of Mathematics Statistics and Computer Science University of Agriculture Makurdi Nigeria Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland Bio Environmental Science Program Morgan State University BaltimoreMD21251 United States Department of Computer Science Ajayi Crowther University Oyo Nigeria

This study examined the use of a social media platform – WhatsApp – by computer science students for learning computing education in the context of a Nigerian education institution. Nowadays, a large community of students in higher education institutions has embraced the WhatsApp platform for social interactions which makes it a useful tool in education. In this study, students formed three closed groups, and each group had a specific computing topic they discussed. Their discussions were in the form of posting questions, providing answers to questions, or expressing knowledge on the group topic. A questionnaire was used to collect data from the participants regarding their experiences. We conducted a descriptive analysis of the students’ learning outcomes. The results show that the use of social media contributes positively to students’ learning achievement, and they are motivated to acquire more knowledge about different computing topics © 2021, Journal of Information Systems Education. All Rights Reserved.

关键词： Students

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