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A CPN-based information propagation model in Online Social Networks

A CPN-based information propagation model in Online Social N...

International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS)

作者： Jinming Kong Wangyang Yu Fei Hao Key Laboratory for Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (纸本)9781665466684

In Online Social Networks (OSNs), information is propagated by interaction among users. A user interacts with others through a variety of behaviors, which can promote the flow of information resource. The previous researches on information propagation lack the analysis of user behavior characteristics and the intuition of the dynamics of information propagation. A OSN system is a distributed concurrent system which is suitable to be modeled graphically with Petri net theory. Taking the Weibo platform as an example, this paper models and simulates its information propagation process using Colored Petri net (CPN). We adopt a modular modeling method. First, based on seven typical user behaviors, namely browsing (no action), posting, reposting, liking, commenting, replying and deleting, we model the information propagation process, which describes the spread of a microblog posted by the initiator of a topic. Then we count the number of actions of each type generated by users during the propagation process, which can evaluate the users' participation for the topic, that is, the popularity of the topic. Finally, we combine the two modules to obtain a complete information propagation model using sharing composition. In addition, we run the model in CPN Tools to simulate the spread of a microblog posted by the initiator of a topic. Moreover, we analyze and verify the state changes and user behavior sequences of the information propagation system using State Space Tools. Our work reveals that it is feasible and advantageous to apply Petri Net theory to social network analysis, so as to discover more information propagation rules in the future.

关键词： Information resources Social networking (online) Computational modeling Petri nets Blogs Ubiquitous computing

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Study of Uv-Vis Molar Absorptivity Variation and Quantitation of Anthocyanins Using Molar Relative Response Factor

SSRN

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SSRN 2023年

作者： Dong, Wen Yang, Xin Zhang, Ning Chen, Pei Sun, Jianghao Harnly, James M. Zhang, Mengliang Department of Chemistry Middle Tennessee State University MurfreesboroTN37132 United States Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Mathematics and Computer Science Fisk University NashvilleTN37208 United States Methods and Application of Food Composition Laboratory Beltsville Human Nutrition Research Center Agricultural Research Service United States Department of Agriculture BeltsvilleMD20705 United States

The effects of anthocyanin’s substitution groups on the UV-Vis molar absorptivity were examined by analyzing a group of 31 anthocyanidin/anthocyanin reference standards with ultra-high performance liquid chromatography-diode array detector (UHPLC-DAD). The hydroxylation and glycosylation on aglycones caused absorbance maxima shift and molar absorptivity change, which was dependent on the substitution position. Such molar absorptivity variation was often neglected in anthocyanin quantitation, resulting in significant analytical errors. A simple yet comprehensive strategy based on the molar relative response factors (MRRFs) and a single master reference calibration (i.e., cyanidin-3-glucoside) was proposed to compensate for the quantitative errors. MRRFs of 617 anthocyanins/anthocyanidins were calculated and the information is freely available at https://***/anthocyanin/. The strategy was applied to analyzing anthocyanins in red cabbage, blueberry, and strawberry samples. The results indicate this approach provides an effective, inexpensive, and accurate method for quantitative analysis of many anthocyanins in food materials without using individual reference standards. © 2023, The Authors. All rights reserved.

关键词： Anthocyanins

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AR-Classroom: Integrating Conversational Artificial Intelligence with Augmented Reality Technology for Learning Spatial Transformations and Their Matrix Representation 54

AR-Classroom: Integrating Conversational Artificial Intellig...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Monjoree, Uttamasha Aguilar, Samantha D. Qian, Chengyuan Van Huyck, Carl Yeh, Shu-Hao Tranbarger, Preston Duane-Tessier, Luke Solitare-Renaldo, Leo Burte, Heather Yasskin, Philip Liew, Jeffrey Song, Dezhen Quek, Francis Yan, Wei Texas A&m University Dept. Architecture College Station United States Texas A&m University Dept. Educational Psychology College Station United States Texas A&m University Dept. Computer Sci. & Eng. College Station United States Texas A&m University Dept. Visualization College Station United States Texas A&m University Dept. Mathematics College Station United States Haverford College Haverford United States Carnegie Mellon University Dept. Computer Science Pittsburgh United States Texas A&m University Dept. Teaching Learning & Culture College Station United States

ISBN: (纸本)9798350351507

This research full paper describes the AR-Classroom application that utilizes augmented reality (AR) and physical and virtual manipulatives to enable undergraduate students to build intuition about the relation between spatial transformations and their mathematical representations. To further build on the app's usability and functionality, additional features are being prototyped to continue improving the user-app interaction with the AR-Classroom. Some of the challenges the students faced when using AR-Classroom were recalling basic matrix operations without geometric context, basic trigonometric functions and their applications in the two-dimensional space, loss of AR registration for not understanding the AR environment, and User Interface (UI) issues. To address these issues, a conversational Artificial Intelligence (AI)-based multi-sensory and interactive assistance has been added to the AR-Classroom. Integrating sophisticated language processing and response generation of AI with immersive three-dimensional capabilities of AR can create a more engaging learning experience than the previous versions of the app. This integration focuses on creating a symbiosis between AR and AI. It creates an elevated user experience by offering real-time, personalized assistance to students dealing with issues related to understanding mathematical concepts and functionalities of the app. A qualitative exploratory usability study was done to assess the user's interaction with the AI implemented in the AR-Classroom, aiming to explore the AI's ability to guide students in using AR technology and aid in introductory matrix algebra learning, to effectively serve the students' learning. Based on the thematic analysis of the user experiment we found four main themes related to users' perceptions of AR-Classroom AI features usability: (1) AI chatbot ease-of-use, (2) Need for answer elaboration from AI, (3) Desire for visual information, and (4) Increased understanding of the content ar

关键词： Students

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Kirchhoff-Law Johnson Noise Meets Web 3.0: A Statistical Physical Method of Random Key Generation for Decentralized Identity Protocols

arXiv

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arXiv 2023年

作者： Chamon, Christiana Mohanasundar, Kamalesh Flanery, Sarah A. Quek, Francis K. Texas A&M University Department of Computer Science and Engineering College StationTX77843 United States Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States Texas A&M University Department of Teaching Learning and Culture College StationTX77843 United States

This paper presents a statistical physical generation of random keys for a decentralized identity ecosystem that uses Web 3.0 protocols. Web 3.0 is driven by secure keys, typically represented in hexadecimal, that are pseudo-randomly generated by an initialization vector and complex computational algorithms. We demonstrate that the statistical physical Kirchhoff-law-Johnson-noise (KLJN) scheme eliminates the additional computational power by naturally generating truly random binary keys to drive the creation of decentralized identifiers (DIDs) that are appended to an Ethereum blockchain. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet protocols

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MedFroDetect: Medicare fraud detection with extremely imbalanced class distributions 33

MedFroDetect: Medicare fraud detection with extremely imbala...

引用

33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020

作者： Su, Yuping Zhu, Xingquan Dong, Bei Zhang, Yumei Wu, Xiaojun Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xian China School of Computer Science Shaanxi Normal University Xi'an China Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton United States

ISBN: (纸本)9781577358213

In this paper, we propose to use machine learning to automate Medicare fraud detection. By cross checking Medicare payment database and provider exclusion database, we build datasets with millions of service providers, including a handful of convicted fraudulent service providers. One essential challenge is that the dataset created is extremely imbalanced, making it extremely difficult to learn accurate classifiers for fraud detection. To tackle the challenge, we first use feature engineering to design effective features, by taking the difference between each service provider and its group cohort into consideration. At the instance level, we also use a synthetic instance generation approach to generate positive samples to alleviate the data imbalance challenge. By combining feature engineering, synthetic instance generation, and under sampling based ensemble learning, our method outperforms baseline approaches for Medicare fraud detection. © FLAIRS *** right reserved.

关键词： Crime

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Towards Emotion-awareness in Programming Education with Behavior-based Emotion Estimation

Towards Emotion-awareness in Programming Education with Beha...

引用

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Yuqian Zhuang Liang Wang Zhiwen Zheng Hao Hu Haijun Wu Xianping Tao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Experimental Teaching Demonstration Center of Computer Science Technology and Software Engineering Nanjing China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Existing studies in both psychology and software engineering have shown the importance of emotions in complex learning and programming tasks. For students who are learning to program, rich emotions are experienced which can provide valuable feedback to their teachers. To accurately model stu-dents' emotions, this paper adopts the well-recognized model of emotions during complex learning that involves four states: engaged, confused, frustrated, and bored. To perform continuous estimation of students' emotions in a non-intrusive manner, this paper proposes to track students' programming behavior and estimate their corresponding emotional states. Compare to the existing approaches on acquiring the students' emotional states with self-reports or bio-sensors, the proposed approach is more feasible in conducting real-world, and large-scale studies for not requiring extensive human interventions or additional devices. Evaluated using data collected from a real-world course project, the proposed approach is showed to be promising for achieving an estimation accuracy of 72.06 % for the above four emotional states. As an enabling technology, the proposed is potentially useful in supporting many applications and improve the quality of programming education in computer science.

关键词： Emotion recognition Computational modeling Education Estimation Psychology Software Mice

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Cultivating Computational Thinking through Game-Based Learning

Cultivating Computational Thinking through Game-Based Learni...

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Frontiers in Education (FIE) Conference

作者： Kelli Adam Robert Lightfoot Mahjabin Chowdhury Jonan Phillip Donaldson Center for Teaching Excellence Texas A&M University College Station United States Department of Computer Science Texas A&M University College Station United States School of Education University of Alabama at Birmingham Birmingham United States

Game-based learning (GBL) can motivate learners and enable them to engage in their learning experience. Additionally, the popularity of GBL has grown in computer science and programming courses, where it can be used to develop computational thinking (CT) skills. CT is critical thinking used to apply fundamental concepts and reasoning derived from computing and computer science. Research has shown that digital GBL can improve critical thinking skills and help learners understand programming concepts, structures, and problem-solving strategies. Despite the popularity of digital GBL, analog card games and bingo games are still valuable and effective tools for learning. However, most studies on GBL that aim to train critical thinking skills have focused on digital approaches. This indicates a gap in the body of knowledge because few studies have explored the effectiveness of analog games for developing CT skills through GBL. The theoretical framework of this study is grounded in two learning theories. Constructivist theory frames learning as the construction of knowledge through a process of action, reflection, and construction. Constructionist learning theory calls for construction of artifacts, learner agency, authentic purpose, or authentic audience during the process. To investigate the design of GBL for developing CT skills and to generate discourse for its widespread adoption, the current study implemented design-based research (DBR) in an undergraduate computer science course for non-computer science majors. By addressing the gap in the literature on GBL for CT skill development, the study aimed to investigate complex learner experiences, including learner struggles and successes, alignment with learning theory principles, and design moves for future iterations of the project. The research question posed is: how can we optimize CT learning through GBL, based on learner experiences and learning theories? Prior to this study, the instructor used GBL in which learne

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Trainable and Explainable Simplicial Map Neural Networks

arXiv

引用

arXiv 2023年

作者： Paluzo-Hidalgo, Eduardo Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain Department of Applied Mathematics I School of engineering University of Seville Seville Spain Department of Computer Science and Artificial Intelligence School of Engineering University of Seville Seville Spain

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper. © 2023, CC BY.

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Splitting criteria for ordinal decision trees: an experimental study

arXiv

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arXiv 2024年

作者： Ayllón-Gavilán, Rafael Martínez-Estudillo, Francisco José Guijo-Rubio, David Hervás-Martínez, César Gutiérrez, Pedro Antonio Department of Computer Science and Numerical Analysis Universidad de Córdoba Campus de Rabanales Ctra. N-IVa Km. 396 Córdoba14071 Spain Department of Quantitative Methods Universidad Loyola Andalucía C. Escritor Castilla Aguayo 4 Córdoba14004 Spain

Ordinal Classification (OC) is a machine learning field that addresses classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as equally distinct, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has implications. Despite this, OC problems are often tackled using nominal methods, leading to suboptimal solutions. Although decision trees are one of the most popular classification approaches, ordinal tree-based approaches have received less attention when compared to other classifiers. This work conducts an experimental study of tree-based methodologies specifically designed to capture ordinal relationships. A comprehensive survey of ordinal splitting criteria is provided, standardising the notations used in the literature for clarity. Three ordinal splitting criteria, Ordinal Gini (OGini), Weighted Information Gain (WIG), and Ranking Impurity (RI), are compared to the nominal counterparts of the first two (Gini and information gain), by incorporating them into a decision tree classifier. An extensive repository considering 45 publicly available OC datasets is presented, supporting © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Introduction of Innovative Educational methods in the Organization of the Education Process of Electrical Engineers 25

Introduction of Innovative Educational Methods in the Organi...

引用

25th IEEE International Conference on Problems of Automated Electric Drive. Theory and Practice, PAEP 2020

作者： Lesia, Sakun Pochtovyuk, Svitlana Shevchuk, Larysa Bilyk, Oleksandr Kremenchuk Mykhailo Ostrohradskyi National University Management Dept. Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Mathematics and Computer Science Dept. Kremenchuk Ukraine Hryhorii Skovoroda University in Pereiaslav Mathematics Computer Science and Teaching Methods Dept. Pereyaslav Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Automation and Computer-integrated Technologies Dept. Kremenchuk Ukraine

ISBN: (纸本)9781728199351

A comprehensive approach to the introduction of innovative teaching methods in the training of future specialists in the field of electrical engineering, based on information and communication technologies was proposed. The experience of countries such as the United States and Japan in the training of highly qualified engineers is taken into account. An example of the use of own software and pedagogical tools that allow you to perform all types of workload of students, future electrical engineers in the study of certain professional disciplines was given. The results of the study can be used in the educational process in the formation of information and professional competencies of future professionals in the field of electrical engineering. © 2020 IEEE.

关键词： Engineers

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