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Enhancing Classification Interpretability with Argument-Based Explanations 16

Enhancing Classification Interpretability with Argument-Base...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Possebom, Ayslan T. Sanches, Hugo E. Aylon, Linnyer B.R. Federal Institute of Paraná 1400 José Felipe Tequinha St. ParanavaíPR 87703-536 Brazil State University of Maringá C56 5790 Colombo Ave MaringáPR 87020-900 Brazil

In computational argumentation theory, an argument is composed of a set of premises that collectively support a conclusion. An argument is often employed in a reasoning process where the internal structure can justify or explain decisions in real-world scenarios. When arguments participate in an argumentation process, the reasoning involves complex, contradictory, or incomplete information. In this paper, we introduce a method for extracting potentially essential arguments from consistent datasets. these arguments serve to explain the classifications produced by supervised machine learning algorithms by identifying the sets of premises that justify the predictions. We applied this method to five datasets, demonstrating how the generated arguments can effectively elucidate classification outcomes. the results showed that our approach can identify the relevant features that explain the possible reasons for the classification. Our findings highlight the method's capability to enhance the interpretability of machine learning models by providing straightforward, clear, and premise-based explanations for their outcomes. © 2025 the Author(s).

关键词： Contrastive learning

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14th international conference on the theory and Application of Diagrams, DIAGRAMS 2024

14th International Conference on the Theory and Application ...

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14th international conference on the theory and Application of Diagrams, DIAGRAMS 2024

ISBN: (纸本)9783031712906

the proceedings contain 44 papers. the special focus in this conference is on theory and Application of Diagrams. the topics include: A Building-Block Approach to the Diversity of Visualization Types – Each Type Expressed Visually, and as a Systematically Generated Sentence;domain-Specific Rules Override Aesthetic Graph Drawing Criteria: An Exploration of User-Generated Diagrams;generating Qualitative Descriptions of Diagrams with a Transformer-Based Language Model;diagram Control and Model Order for Sugiyama Layouts;b42: the Geometry of 4-Valued Contradiction;a Way Diagrams Explain: Analysis Based on Consequence Matching;euler Diagrams, Aristotelian Diagrams and Syllogistics;what Does It Mean that Diagrams Represent Constructions?;the Topology of Assertion: A Diagrammatic Rationale for Our Enduring Love of Truth;schopenhauer’s Sorites Diagram;category theory for Aristotelian Diagrams: the Debate on Singular Propositions;rectangular Euler Diagrams and Order theory;reference by Occurrence;eulerMerge: Simplifying Euler Diagrams through Set Merges;representing Uncertainty with Expanded Ueberweg Diagrams;indeterminate Set Space Diagrams;can Euler Diagrams Improve Syllogistic Reasoning in Large Language Models?;mozi’s Square of Opposition and Logemes as New Logical Approach;implicational Existential Graphs;aristotelian Diagrams as Logic Diagrams;sentence Negation and Term Negation as Syntactic Operations in Diagram Logic;playing Games with Diagrams: Truth Diagrams and Game Semantics;peirce’s Extended Euler Diagrams and the System Atl Based on Ladd-Franklin’s Exclusion Relations;anxiety Moderates the Effects of Drawing Support on Drawing Accuracy in Mathematical Modeling;learning Magnitudes of Energy Consumption with Symbolic or Iconic Representations;designing a Mind-Mapping-Assisted Comparative Literature Course in Chinese Academic Settings;integration of learning through the Use of Self-constructed Diagrams: Opportunities and Challenges;chinese Children’ Drawing in Sci

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED 14

A High-Precision Edge Detection Algorithm Based on Improved ...

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14th IEEE international conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Yuan, Yeborui Chen, Wenyuan Tang, Jie Yang, Jia School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics 110016 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9798331506056

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. the algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. the experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. this work provides an important reference for the further development of the edge detection technology. © 2024 IEEE.

关键词： Contrastive learning

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Factors Influencing Co-creation of Open Education Resources using learning Object Repositories 14

Factors Influencing Co-creation of Open Education Resources ...

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14th international conference on Computer Supported Education (CSEDU)

作者： Chanda, Mulenga Chirwa, Elizabeth Kamanga, Mirriam Kayula, Mwenya Phiri, Lighton Univ Zambia Dept Lib & Informat Sci Lusaka Zambia

ISBN: (纸本)9789897585623

Open Education Resources (OERs) have been identified as instrumental in facilitating access to quality teaching and learning materials. However, despite the rapid development of technologies capable of making OERs, the uptake and co-creation of OERs by educators is still low. this paper outlines a study conducted in order to investigate the use of learning Object Repositories (LORs) in co-creation of OERs by educators. Using the Uses and Gratification theory, a survey was conducted with 36 educators in order to identify learning objects commonly used by educators and, additionally, to determine the factors that can positively influence educators to be co-creators of OERs. the results indicate that sharing knowledge, contributing to other people and intellectual challenge are factors that are highly correleted in motivating educators to be co-creators of OERs. the results also indicate that the learning objects commonly used by the educators are the course modules and presentation slides. In conclusion, in order for educators to be co-creators of OERs they need to be intrinsically motivated and be able to collaborate with others for non-selfish reasons. the results of of this study have the potential of influencing how LORs should be design in order to facilitate effective co-creation of OERs.

关键词： learning Object Repositories Open Education Resources OERs Repositories

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Building an English learning Community Model Assisted by AI and VR Technologies

Building an English Learning Community Model Assisted by AI ...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Guangzhen Xi'an Fanyi University Shaanxi province Xi'an710105 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

With the further promotion of information technology in universities, the continuous enrichment of online teaching resources, and the continuous standardization of online teaching management, online learning as a main learning method has been increasingly accepted by more and more people. this article selects fuzzy neural networks for modeling and constructs a network learning evaluation system based on the characteristics of network learning evaluation. the English teaching classroom based on artificial intelligence and virtual reality proposed in this article can provide scarce educational resources while improving students' learning outcomes. the experimental results show that the method proposed in this article has a higher level of acceptance among students compared to traditional teaching methods, with a 12.7% higher learning efficiency among students. It is feasible in theory and reliable in practice, and can be used as an intelligent computing model for developing network learning evaluation systems. It has good promotional value and provides a new evaluation method for network learning evaluation. © 2025 SPIE.

关键词： Federated learning

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Introduction to Advances in Information, Intelligence, Systems and Applications 14th

Introduction to Advances in Information, Intelligence, Syste...

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14th international conference on Information, Intelligence, Systems, and Applications, IISA 2023

作者： Tsihrintzis, George A. Virvou, Maria Bourbakis, Nikolaos G. Jain, Lakhmi C. Department of Informatics University of Piraeus Piraeus185 34 Greece Department of Electrical and Computer Engineering Wright State University DaytonOH45435-0001 United States KES International Shoreham-by-Sea United Kingdom

ISBN: (纸本)9783031674259

Information is widely available and accessible, but frequently leads to information overload and overexposure and the effort for coding, storing, hiding, securing, transmitting, and retrieving it may be excessive. Intelligence, inspired by biological and other paradigms, is required to manage information and extract knowledge from it. Information and Multimedia Systems, with an increasing level of Intelligence, are being developed that incorporate these advances. As a result, new Technologies, Protocols, and novel Applications are emerging. In turn, the novel applications lead to the collection of new information and the "Information → Intelligence → Systems → Applications" circle is repeated at ever higher levels. this book includes extended versions of selected research works that were presented at the 14th international conference on Information, Intelligence, Systems, and Applications (IISA2023), Volos, Greece, July 10–12, 2024 (https://***/iisa2023) along with additional relevant papers by invited authors. these works cover a broad spectrum of topics from both novelties in theory and advanced methodologies as well as applications in the Energy, Agriculture, Medicine, Forensics, Healthcare, Education, Tourism, Inspection, Traffic and Software Engineering. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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14th international conference on Information, Intelligence, Systems and Applications, IISA 2023

14th International Conference on Information, Intelligence, ...

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14th international conference on Information, Intelligence, Systems and Applications, IISA 2023

ISBN: (纸本)9798350318067

the proceedings contain 128 papers. the topics discussed include: step by step building and evaluation of low-cost capacitive technology touch system for human- social robot interaction;energy sector digitalization: a security framework application for role-based access management;application of big data analytics in the electrical sector: a real case study;a study on rewarding mechanisms for activating silent users in social media;a combination of genetic algorithms and local search to solve a real data university timetable scheduling problem;applying DOI theory to assess the required level of explainability in artificial intelligence-empowered medical applications;implementation of a small scale smart greenhouse structure using fuzzy logic and IOT;on beat tracking and tempo estimation of musical audio signals via deep learning;automated binary program partitioning through structural analysis;and modelling data-driven digital twins of EV batteries for predictive analytics.

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Classification of Abhorrence Facial Expressions Using Convolutional Neural Network with Comparison with Recurrent Neural Networks for Better Accuracy 14

Classification of Abhorrence Facial Expressions Using Convol...

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14th international conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Muzakirlaikhkhan, P. Gayathri, A. Arumugam, I. Meignana Saveetha Univ Dept Comp Sci Chennai 602105 Tamil Nadu India Saveetha Univ Saveetha Dent Coll Hosp Sci SIMATS Chennai Tamil Nadu India

ISBN: (纸本)9781665460712

the purpose of this research is to determine the intense dislike of facial features using a facial image for ML techniques. the goal of this study is to improve the reliability of the results. In terms of expressions, on the face Materials and Procedures: 104 samples have been used, implementing the two algorithms with 80% value, and heart images of patients were obtained from several online with the most recent study findings. the CNN algorithmic method produced 92% accurate results. for facial detection in this investigation. Using independent sample t-tests, there is a statistically significant distinction in accuracy between the two algorithms of p0.05 (p=0.004). As CNN algorithmic is greater the RNN algorithm

关键词： Facial Expression Innovative Convolutional Neural Networks Pixel Value Method Machine learning Accuracy Novel Classifier Approach

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Exposing the Attraction of Data- An Examination and Application of Teaching Data Visualization in Multimedia learning Mode From theory to Practice 14

Exposing the Attraction of Data- An Examination and Applicat...

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14th international conference on Information Technology in Medicine and Education, ITME 2024

作者： Wang, Yue Liu, Mingyou Zhu, Yingxue Liu, Sitong Guizhou550001 China University of Nottingham Ningbo315000 China

ISBN: (纸本)9798350356670

With the advent of the big data era, data visualization technology has grown in significance and become an essential core technology in the fields of statistical analysis and decision support. However, the current approaches of teaching the Data Visualization course tend to downplay the significance of theoretical knowledge in favor of developing students' practical skills with various information visualization technologies. When students are faced with complex facts, this teaching style, which prioritizes practice over theory, may result in a lack of deep analytical skills and creative thinking. Meanwhile, in order to reveal the charm of data, the process of transforming data information into visual form can significantly improve the presentation effect of data, and observe and browse the data in an intuitive and interactive way, so that it is easier to find the hidden features, relationships, and patterns in the data, which can provide powerful support for data analysis and *** order to create a naturally occurring combination of knowledge and application and better prepare students for the difficulties of the big data era, teaching Data Visualization educational programs using a blended learning approach requires maintaining a balance between the two. © 2024 IEEE.

关键词： Data assimilation

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Optimizing Multi-variable Time Series Forecasting Using Metaheuristics 1

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14th international conference on Metaheuristics (MIC)

作者： Zito, Francesco Cutello, Vincenzo Pavone, Mario Univ Catania Dept Math & Comp Sci Vle Andrea Doria 6 I-95125 Catania Italy

ISBN: (数字)9783031265044

ISBN: (纸本)9783031265037;9783031265044

Multi-variable time series forecasting is one of several applications of machine learning. Creating an artificial environment capable of replicating real-world behavior is useful for understanding the intrinsic relationship between variables. However, selecting a predictor that ensures good performance for variables of different natures is not always a simple process. An algorithmic approach based on metaheuristics could be a good alternative to find the best predictive model for variables. Each predictor is optimized for forecasting a particular variable in a multi-agent artificial environment to improve the overall performance. the resulting environment is compared with other solutions that use only the same type of predictor for each variable. Finally, we can assert that using a multi-agent environment can improve the performance, accuracy, and generalization of our model.

关键词： Metaheuristic Optimization Machine learning Multi-variable time series prediction Neural network Artificial forecasting

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