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Student-Mediated Knowledge Exchange in Switzerland 4th

Student-Mediated Knowledge Exchange in Switzerland

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4th International Conference on Society 5.0

作者： Barjak, Franz Heimsch, Fabian Univ Appl Sci & Arts Northwestern Switzerland FHN Sch Business Riggenbachstr 16 CH-4600 Olten Switzerland

ISBN: (纸本)9783031714115;9783031714122

The majority of the academic work on knowledge and technology transfer has been on two types, knowledge commercialization and academic engagement. Mechanisms which involve students have been neglected though they are as common and potentially as beneficial. This neglect harbors several risks with regard to the economic and social valorization of research results. We define and typify the construct of student-mediated knowledge exchange and review the literature which has reported multiple benefits for the involved parties, students, universities and companies, but also some costs. We then use survey data on two selected measures generated in a survey of the institutes of 18 Swiss higher education institutions (HEIs) for multivariate regression analyses at institute level to explain the differences for the student-mediated knowledge exchange metrics with structural characteristics of the institutes (university type, size, academic discipline) and variables on their activities (teaching focus, cooperation and commercialization orientation). The results show that student-mediated transfers capture knowledge exchange with companies that is not covered by the common metrics for knowledge commercialization and academic engagement. In sum, we argue that the scope of knowledge and technology transfer metrics should be expanded to include measures that capture student-mediated forms.

关键词： Student-mediated knowledge exchange university-industry cooperation knowledge transfer metrics teaching-oriented universities

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Bayesian Parameter Inference in Stochastic Biochemical Models Using Moment Approximations 37th

Bayesian Parameter Inference in Stochastic Biochemical Model...

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37th International Conference on computer Applications in Industry and Engineering

作者： Hossain, Kannon Sidje, Roger B. Univ Alabama Dept Math Tuscaloosa AL 35487 USA

ISBN: (纸本)9783031762727;9783031762734

The chemical master equation (CME) is a mathematical tool utilized to model the stochasticity of the complex biochemical reaction networks. As the direct solution of the CME is notoriously expensive, moment-based approximations are computationally attractive in terms of time and memory to compute the statistics of the CME. The Bayesian method with delayed rejection adaptive Metropolis (DRAM) sampler provides an accurate probabilistic framework for parameter inference, reducing the need for costly computations of likelihood functions. In this study, we derive a system of ordinary differential equations (ODEs) using zero-moment approximations and employ the DRAM sampler algorithm to find the approximate posterior distributions of the model parameters. The quality of the method is evaluated through two systems biology case studies, with the aim of enabling efficient data-driven inferences for more complex biochemical models.

关键词： Bayesian inference Markov chain Monte Carlo (MCMC) moment approximations chemical master equation

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Political Bias of Large Language Models in Few-Shot News Summarization 5th

Political Bias of Large Language Models in Few-Shot News Sum...

引用

5th International Workshop on Algorithmic Bias in Search and Recommendation

作者： Onishi, Takeshi Caverlee, James Toyota Motor Co Ltd Frontier Res Ctr Toyota Japan Texas A&M Univ Dept Comp Sci & Engn College Stn TX USA

ISBN: (纸本)9783031719745;9783031719752

As Large Language Models (LLMs) have become robust and universal in various tasks and increasingly popular and widely used in society, their political bias is becoming more and more critical. Hence, to probe the political bias of LLMs, several studies have proposed frameworks that ask political questions and estimate the political stance of LLMs by using their answers. However, these approaches suffer from the effect of the question's wording and the randomness of answer generation algorithms, and do not take into account information selection bias in the context of summarization tasks. In this work, we summarize news articles describing the same news event with different political stances by using the LLMs and evaluate the political stance of the output summaries by comparing the output and input texts. We probed some LLMs and found a potential political bias in word choice. Additionally, our result indicates that the bias can be mostly dissipated by generation algorithms.

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Adaptive Exploration: Elevating Educational Impact of Unsupervised Knowledge Graph Question Answering 7th

Adaptive Exploration: Elevating Educational Impact of Unsupe...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yang, Xi Chen, Zhangze Guo, Hanghui Shestakevych, Tetiana Lviv Polytech Natl Univ Lvov Ukraine Zhejiang Normal Univ Coll Educ Jinhua Zhejiang Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua Zhejiang Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Question answering (QA) systems have been widely used in educational domain, significantly contributing to immediate information access and enhancing learning experiences. This paper introduces an efficient educational Knowledge Graph Question Answering (KGQA) framework that operates without relying on annotated training data. Our method enables the swift deployment of new knowledge graphs by simulating human-like information acquisition through a symbolic exploration module. Leveraging diverse program generation and large language models (LLMs), we formulate natural language questions to optimize the query for each program. To address semantic accuracy challenges in LLMs due to the absence of contextual training data, we propose adaptive strategies, including dynamic contextual re-ranking. These techniques significantly enhance question generation precision, showcasing robust performance in unsupervised settings. The framework demonstrates exceptional adaptability across varied queries and outperforms state-of-the-art models in zero-shot queries across various knowledge graph scales and datasets, underscoring its efficacy and scalability.

关键词： Knowledge Graph Question Answering Large Language Models

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The Research on the Multi-agent Argumentation Semantics 4th

The Research on the Multi-agent Argumentation Semantics

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Liu, Yunxiang Lan, Ting Cui, Jianying Sun Yat Sen Univ Inst Logic & Cognit Dept Philosophy Guangzhou Peoples R China

ISBN: (纸本)9789819603534;9789819603541

In multi-agent argumentation scenarios, considerations such as the strength of attacks on arguments, the number of attacks received, and different criteria for selecting arguments in different argumentative scenarios need to be taken into account. To this end, this paper proposes various multi-agent argumentation semantics. Firstly, various types of multi-agent argumentation semantics including extension-based and ranking-based are proposed to characterize the ways in which arguments are selected by multiple agents in different scenarios. Secondly, methods such as constructing framework-updating models are used to study the relationships between these different semantics, providing theoretical references for the selection of multi-agent argumentation semantics.

关键词： Abstract Argumentation Framework Multi-agent Argumentation Multi-agent Argumentation semantics

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Optimisation-Based Classification Tree: A Game Theoretic Approach to Group Fairness 7th

Optimisation-Based Classification Tree: A Game Theoretic App...

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7th International Conference on Optimization and Learning

作者： Liapis, Georgios I. Papageorgiou, Lazaros G. UCL Univ Coll London Dept Chem Engn Sargent Ctr Proc Syst Engn Torrington Pl London WC1E 7JE England

ISBN: (纸本)9783031779404;9783031779411

The growing use of machine learning algorithms in decisions that significantly affect people necessitate interpretable and fair approaches. Mathematical programming based machine learning models have attracted attention because of the flexibility they provide to integrate features like interpretability and fairness, combined with high accuracy. This work introduces a mathematical programming based classification tree that uses a game theoretic approach to address group fairness. The proposed mathematical formulation is a Mixed Integer Linear Programming model using a piecewise linearisation strategy based on special-ordered sets. The overall misclassification rate is the fairness metric examined and the Nash bargaining scheme is followed to balance the trade off between the misclassification error of the groups. The efficiency of the methodology is evaluated via three binary and multi-class literature datasets, which provide evidence for the fairness and accuracy of the predictions made by the model.

关键词： Machine learning Fairness Mathematical programming Data classification Game theory

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An Analysis of Different information Granularity Distribution Protocols to Improve Consistency in Intuitionistic Reciprocal Preference Relations 8th

An Analysis of Different Information Granularity Distributio...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Gonzalez-Quesada, Juan Carlos Herrera-Viedma, Enrique Alonso, Sergio Morente-Molinera, Juan Antonio Javier Perez, Ignacio Javier Cabrerizo, Francisco Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence DaSCI Granada 18071 Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Software Engn DaSCI Granada 18071 Spain

ISBN: (纸本)9783031856273;9783031856280

Recently, an optimization of information granularity distribution has been proposed as a way in which the consistency of an intuitionistic reciprocal preference relation can be increased. Specifically, a symmetric and uniform distribution of information granularity has been taken into account for intuitionistic reciprocal preference relations. However, there are other information granularity distribution protocols that could also be used. The objective of this paper is to analyze all of them and determine their effectiveness in improving the consistency of an intuitionistic reciprocal preference relation. The performance of the protocols is discussed by conducting some numerical studies that help provide insights into the effectiveness of the protocols to increase the consistency.

关键词： information granularity Atanassov's intuitionistic fuzzy sets Preference relations Consistency Fuzzy decision-making

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A Benchmark for Missing Data Imputation Techniques: Development Perspectives and Performance Comparative 7th

A Benchmark for Missing Data Imputation Techniques: Developm...

引用

7th International Conference on Optimization and Learning

作者： Cabrera-Sanchez, Juan-Francisco Cruz-Corona, Carlos Escolano, Andres Yanez Silva-Ramirez, Esther-Lydia Univ Cadiz Dept Comp Sci & Engn Puerto Real Spain Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9783031779404;9783031779411

Knowledge extraction from information stored in databases is always subject to the presence of missing values. Missing data is an unavoidable problem that affects many disciplines of researchers and data scientists. Inasmuch as machine learning algorithms cannot work with incomplete data in the data sets, data imputation is an essential task to obtain quality data. This research approach provides an overview of the data missingness mechanism and the process of generating synthetic missing data, the imputation of all types of variables, and the performance assessment of several imputation methods. Traditional algorithms, Machine Learning methods and various Autoencoder-based deep learning architectures have been studied. An exhaustive analysis and comparison of 21 heterogeneous data sets in various areas has been proposed. They have been exposed to a perturbation procedure with different missingness mechanisms and various missingness rates, covering the different possibilities that can occur in real life. The experimental results show that deep learning models outperform the other methods studied. Furthermore, the performance of data imputation methods does not depend on the missingness mechanism or the synthetic missingness generation method used nor on the percentage of missing values.

关键词： Missing data Machine Learning Deep Learning Autoencoders

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HOTS: A Containers Resource Allocation Hybrid Method Using Machine Learning and Optimization 7th

HOTS: A Containers Resource Allocation Hybrid Method Using M...

引用

7th International Conference on Optimization and Learning

作者： Leclercq, E. Rivalan, Jonathan Smile Ile De France France

ISBN: (纸本)9783031779404;9783031779411

One of the main challenges in cloud computing is resource management, the ability to schedule workloads and services over the infrastructure in the most automated way. By optimizing cloud assignment and resource usage, energy can be saved, production incident can be anticipated and services QoS improved. With the recent years emergence of light virtualisation, known as containerization, the resource allocation problem was brought back, notably to support containers elasticity, hence the dynamic allocation of ressource at runtime at a single service scale. In this paper we show that using an hybrid loop system, which combines unsupervised learning and optimization techniques, our algorithm provides and iteratively improves scheduling solutions to containers resource assignment, enabling capacity planning over dynamic resource loads. Within our benchmarks, these solutions outperform state of the art algorithms, by an average of 6.3%, while providing more expressivity and control over input parameters. We describe also the implementation of this method, through an open source Python library called HOTS, which allows hybrid optimization for time series based use cases.

关键词： Clustering Continuous Optimization Containerized Applications Resource Allocation

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Enhancing Cross-Cultural Teaching and Learning: An Instructor's View on Africanization 4th

Enhancing Cross-Cultural Teaching and Learning: An Instructo...

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4th International Conference on Society 5.0

作者： Eybers, Sunet Kroeze, Jan H. van Staden, Corne J. Univ South Africa Johannesburg South Africa

ISBN: (纸本)9783031714115;9783031714122

Research has indicated that learners who receive education in a context they can relate to are more successful in acquiring knowledge and skills. For this reason, educators in Africa often explore the concept of Africanization as part of cross-cultural teaching and learning efforts. Africanization includes local and indigenous cultural considerations to instill African culture and identity in teaching and learning environments. This qualitative study investigates the perceptions of academic staff, focusing on the effectiveness of using an African case study to explain important theoretical constructs in an undergraduate Business Analysis and a Human-computer Interaction course at a higher-educational institution. Thematic analysis performed on the results of in-depth semi-structured interviews involving academic instructors reveals seven main themes, with the theme on the relevance and interpretation of practical examples occurring most frequently. The findings suggest that the Africanization of content, using local and Afrocentric examples, can assist learners in acquiring relevant knowledge and skills and be useful in cross-cultural teaching and learning, as learners can relate the theory to their local context. However, the concept should be presented to learners before introducing new academic content.

关键词： cross-cultural Africanization decolonization teaching and learning case study business analysis human-computer interaction

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