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9th International Conference on Design, User Experience, and Usability, DUXU 2020, held as part of the 22nd International Conference on Human-computer Interaction, HCII 2020

作者： Huang, Zhao Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China

ISBN: (纸本)9783030497590

Advanced mobile technologies have enabled the thousands of applications to be developed that can be used by people on their move. The ability to easily use and be satisfied with mobile apps is important to users. However, the usability requirements of mobile apps differ due to their specific genres, design purposes, business goals and strategies. Therefore, this study conducts a systematic review of extant literature related to mobile app design, and thereby classifies usability pertaining to usability requirements and user experience in a variety of categories of mobile apps. We believe that it would be useful for researchers and practitioners to better understand mobile app usability. © 2020, Springer Nature Switzerland AG.

关键词： Usability engineering

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Dual-Weighted Multiview Clustering Based on Anchor

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Concurrency and Computation: Practice and Experience 2025年第15-17期37卷

作者： Yan Zhang Yali Peng Shengnan Wu Shigang Liu Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

In the field of multiview clustering, how to make full use of information from multiple data sources to improve the clustering performance has become a hot research topic. However, the rapid growth of high-dimensional multiview data brings great challenges to the research of multiview clustering algorithms, especially the time and space complexity of the algorithms. As an effective solution, anchor-based technique has gained wide attention in large-scale multiview clustering tasks. Nevertheless, the current anchor-based methods fail to fully take into account the importance of different views and the difference and diversity of anchors at the same time, which limits the clustering performance to some extent. To address these problems, we propose a dual-weighted multiview clustering based on anchor (DwMVCA). First, we effectively distinguish the different impacts of high-quality and low-quality views on clustering by adaptively learning the weights of different views. Second, by introducing the adaptive weighting matrix of anchors and self-correlation matrix regularization term, the difference and diversity of anchors are fully considered to effectively reduce the effect of redundant information on clustering. Furthermore, we design a three-step alternating optimization algorithm to solve the resultant optimization problem and prove its convergence. Extensive experimental results show that the proposed DwMVCA has obvious advantages in clustering performance on large-scale datasets, especially on datasets with more than 100,000 samples that still maintain linear time complexity.

关键词： anchor points multiview clustering subspace clustering weighting

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When to update your model: constrained model-based reinforcement learning 22

When to update your model: constrained model-based reinforce...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Tianying Ji Yu Luo Fuchun Sun Mingxuan Jing Fengxiang He Wenbing Huang Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences JD Explore Academy *** Inc Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9781713871088

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed.

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Levelized Cost of Energy Optimization Method for the Dish Solar Thermal Power Generation System 15th

Levelized Cost of Energy Optimization Method for the Dish So...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Dang, Genye Su, Hongsheng Yue, Biao School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou China Experimental Teaching Center on Computer Science Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9783030324551

In view of the high cost of power generation and the shortcomings of scale and industrialization of dish-Stirling optical thermal power station, the NSGA-II algorithm is proposed to optimize and analyze levelized cost of energy for dish solar thermal power generation system. The capacity and cost model of dish-Stirling thermal power generation system is established, and the influence of power station capacity, thermal storage medium and thermal storage time on the levelized cost of energy is analyzed by using NSGA-II genetic algorithm and particle swarm optimization. The results show that the larger the scale of power generation, the lower the levelized cost of energy, and the thermal storage medium and the thermal storage time have a greater impact on the levelized cost of energy. © Springer Nature Switzerland AG, 2020.

关键词： Particle swarm optimization (PSO)

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Lessons learned from Successful Online computer science PD Completers in the Midst of a Pandemic

Lessons learned from Successful Online Computer Science PD C...

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Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT)

作者： María G. Arreguín Priya V. Prasad Michael J. Zawatski Jina Wilde Darean Wilde Amanda S. Fernandez Crystal Kalinec-Craig Emily P. Bonner Edward Monsibaiz Timothy T. Yuen Interdisciplinary Learning and Teaching University of Texas at San Antonio San Antonio USA Mathematics University of Texas at San Antonio San Antonio USA Computer Science University of Texas at San Antonio San Antonio USA

The intended audience of this experience report include computer science coordinators, computer science teachers and computer science (CS) project leaders who seek to increase Latinx participation in computer science through preparing secondary teachers to teach CS. This report narrates the challenges faced and lessons learned while designing and implementing in-service teacher professional development, CS4SA-NOW, in an online format. While the pandemic affected the completion rate of most participants, CS4SA-NOW coordinators synthesized lessons learned based on two cases involving teachers who successfully completed a series of professional development CS modules. Traits identified in these teacher participants included pedagogical and ideological clarity as well as an asset-oriented mindset. Lessons learned from this project can inform future designs of in-service teacher professional development.

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Towards robust results in Multi-Criteria Decision Analysis: ranking reversal free methods case study 26

Towards robust results in Multi-Criteria Decision Analysis: ...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Wieckowski, Jakub Król, Robert Watróbski, Jaroslaw Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Doctoral School of University of Szczecin ul. Mickiewicza 16 Szczecin70-383 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

The practical capabilities of decision support systems are beneficial in many complex problems. They aim to equip the decision-maker with knowledge about the preferred choices from the set under consideration. Often, there are minor differences between the alternatives analyzed, making it necessary to determine whether changes in the parameters characterizing them can influence their final position. For this purpose, a sensitivity analysis of the solutions can be used to examine the resistance of the results to changes. In this paper, we address the problem of performing the sensitivity analysis using MCDA methods. Most studies focus on testing the sensitivity of rankings of popular methods. However, their main disadvantage is their susceptibility to the phenomenon of ranking reversal. It may distort the results obtained in a sensitivity analysis. Therefore, it is worth using methods resistant to the ranking reversal phenomenon for this purpose. The SPOTIS and COMET methods were used in a theoretical multi-criteria problem, where the calculated results were then subjected to sensitivity analysis. The study showed that the SPOTIS method tends to be more robust to the computed results. © 2022 The Authors. Published by Elsevier B.V.

关键词： Sensitivity analysis

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Accuracy of the TOPSIS Method with Different Input Data 26

Accuracy of the TOPSIS Method with Different Input Data

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Król, Robert Wieckowski, Jakub Watróbski, Jaroslaw Doctoral School of University of Szczecin ul. Mickiewicza 16 Szczecin70-383 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings. © 2022 The Authors. Published by Elsevier B.V.

关键词： Fuzzy logic

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An extension method of sub-health intelligent prediction and transformation

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Procedia computer science 2022年 214卷 1554-1560页

作者： Junwen Sun Xingsen Li Zhengxiang Zhu Junlin Zeng Institute of Extenics and Innovation Methods Guangdong University of Technology Guangzhou 510006 China School of electromechanical engineering Guangdong University of Technology Guangzhou 510006China Consumer Finance Center Bank of NanJing NanJing 210002 China School of Computer Science and Technology Guangdong University of Technology Guangzhou 510006China

With the development of people's growing emphasis on health and the improving personal intelligent devices, wearable intelligent health monitoring devices have been popularized, and the data detected by them have great application potential in the prediction and improvement of sub-health. The current situation of wearable intelligent health monitoring devices is summarized, and the data of existing products and the data processing methods are listed. In order to study the relationship between physiological health data and sub-health, a construction method of decision tree and transformation tree based on extension theory is proposed, which enables it to intelligently acquire the rule knowledge of state transformation from the perspective of dynamic, and evaluate and analyze it through the superiority evaluation.

关键词： Sub-health Intelligence Health monitoring Extenics Decision Tree Transformation tree

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Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables

arXiv

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arXiv 2024年

作者： Alamu, Opeyemi Sheu Choque, Bismar Jorge Gutierrez Rizvi, Syed Wajeeh Abbs Hammed, Samah Badr Medani, Isameldin Elamin Siam, Md Kamrul Tahir, Waqar Ahmad Departmet of Statistics Federal College of Animal Health and Production Technology Ibadan Nigeria Department of Science Universidad Mayor de San Andres La Paz Bolivia Department of Data Science National University of Computer & Emerging Sciences-FAST City Campus Karachi Pakistan Department of Basic Medical Science University of Khartoum Khartoum Sudan Consultant Obstetrics and Gynecology University Hospital The University of Jazan Jazan Saudi Arabia New York Institute of Technology New York United States Department of Internal Medicine/Casualty Khalifa Gull Nawaz Teaching Hospital Bannu Pakistan

Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study employs survival analysis techniques, including Cox proportional hazards and parametric survival models, to enhance the prediction of the log odds of survival in breast cancer patients. Clinical variables such as tumor size, hormone receptor status, HER2 status, age, and treatment history were analyzed to assess their impact on survival outcomes. Data from 1557 breast cancer patients were obtained from a publicly available dataset provided by the University College Hospital, Ibadan, Nigeria. This dataset was preprocessed and analyzed using both univariate and multivariate approaches to evaluate survival outcomes. Kaplan-Meier survival curves were generated to visualize survival probabilities, while the Cox proportional hazards model identified key risk factors influencing mortality. The results showed that older age, larger tumor size, and HER2-positive status were significantly associated with an increased risk of mortality. In contrast, estrogen receptor positivity and breast-conserving surgery were linked to better survival outcomes. The findings suggest that integrating these clinical variables into predictive models improves the accuracy of survival predictions, helping to identify high-risk patients who may benefit from more aggressive interventions. This study demonstrates the potential of survival analysis in optimizing breast cancer care, particularly in resource-limited settings. Future research should focus on integrating genomic data and real-world clinical outcomes to further refine these models. © 2024, CC BY.

关键词： Lung cancer

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Systematizing Modeler Experience (MX) in Model-Driven Engineering Success Stories

arXiv

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arXiv 2024年

作者： Kalantari, Reyhaneh Oertel, Julian Exelmans, Joeri Rukmono, Satrio Adi Amaral, Vasco Tichy, Matthias Juhnke, Katharina Steghöfer, Jan-Philipp Abrahão, Silvia Department of Engineering Design and Teaching Innovation University of Ottawa 800 King Edward Avenue OttawaK1N6N5 Canada Department of Software Engineering University of Rostock Albert-Einstein-Straße Rostock18057 Germany Department of Computer Science University of Antwerp Middelheimlaan 1 Antwerp2020 Belgium Department of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven5600 MB Netherlands Department of Computer Science NOVA School of Science & Technology Campus de Caparica Caparica2829-516 Portugal Institute of Software Engineering and Languages Ulm University James-Franck-Ring Ulm89081 Germany SMT-EMI6 Carl Zeiss SMT GmbH Rudolf-Eber-Straße 2 Oberkochen73447 Germany XITASO GmbH Austraße 35 Augsburg86153 Germany Instituto Universitario Mixto de Tecnología Informática Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of "modeler experience" (MX). MX includes factors such as user experience, motivation, integration, collaboration & versioning and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling. © 2024, CC BY.

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