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检索条件"任意字段=14th International Conference on Algorithmic Learning Theory"

共 834 条记录，以下是121-130 订阅

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Bridging theory and Practice in Outcome-Based Assessment: Developing a Web Application for learning Outcome Alignment 14

Bridging Theory and Practice in Outcome-Based Assessment: De...

引用

14th international conference on Educational and Information Technology, ICEIT 2025

作者： Alhazmi, Abdulsalam K. University of Science and Technology Faculty of Engineering & Computing Aden Yemen

ISBN: (纸本)9798331540883

Assessment remains a cornerstone of the educational process, with standardized testing often serving as a primary method for evaluating learning. However, as pedagogical approaches continue to evolve, Outcome-Based Assessment (OBA) has gained prominence as a more comprehensive model that aligns learning outcomes with teaching strategies. this paper explores the significance of OBA, grounded in frameworks of domain learning, and highlights its global adoption as a measure of institutional quality. By focusing on measurable learning outcomes across cognitive domains, OBA enhances student engagement, critical thinking, and skill development. Extending this theoretical foundation, the paper presents an Outcome Based Assessment System (OBAS) developed to facilitate the practical implementation of OBA. OBAS provides a structured framework that links program learning outcomes to course learning outcomes, integrating considerations of learning domains and difficulty levels. By supporting the creation of standardized and authentic OBA assessments, this application empowers educators and institutions to uphold educational quality, foster accountability, and align assessments with targeted learning objectives. © 2025 IEEE.

关键词： Teaching

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Expander Decomposition in Dynamic Streams 14

Expander Decomposition in Dynamic Streams

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14th Innovations in theoretical Computer Science conference, ITCS 2023

作者： Filtser, Arnold Kapralov, Michael Makarov, Mikhail Bar-Ilan University Ramat-Gan Israel EPFL Lausanne Switzerland

ISBN: (纸本)9783959772631

In this paper we initiate the study of expander decompositions of a graph G = (V, E) in the streaming model of computation. the goal is to find a partitioning C of vertices V such that the subgraphs of G induced by the clusters C ∈ C are good expanders, while the number of intercluster edges is small. Expander decompositions are classically constructed by a recursively applying balanced sparse cuts to the input graph. In this paper we give the first implementation of such a recursive sparsest cut process using small space in the dynamic streaming model. Our main algorithmic tool is a new type of cut sparsifier that we refer to as a power cut sparsifier - it preserves cuts in any given vertex induced subgraph (or, any cluster in a fixed partition of V ) to within a (δ, ϵ)-multiplicative/additive error with high probability. the power cut sparsifier uses Õ(n/ϵδ) space and edges, which we show is asymptotically tight up to polylogarithmic factors in n for constant δ. © Arnold Filtser, Michael Kapralov, and Mikhail Makarov;licensed under Creative Commons License CC-BY 4.0.

关键词： Graph theory

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Fuzzy Kernel Extreme learning Machine for Face Recognition 2022

Fuzzy Kernel Extreme Learning Machine for Face Recognition

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14th international conference on Contemporary Computing, IC3 2022

作者： Ahuja, Bhawna Vishwakarma, Virendra P. Guru Gobind Singh Indraprastha University Delhi India

ISBN: (纸本)9781450396752

Kernel Extreme learning Machine (KELM) provides deterministic solution to pattern recognition problems in non-iterative manner. On contrary to ELM, the feature mapping is implicitly accomplished in KELM by practicing kernel matrix function. the performance of a learning technique relies on the capability of feature vectors. Keeping this perspective, we have applied fuzzy concept for feature extraction to develop an enhancement of KELM named as Fuzzy KELM (FKELM). In the introduced technique, the capability of fuzzy set is utilized to obtain the association of pixel elements of facial images with distinct classes. the validity of presented technique is tested on face databases. the empirical results illustrates that the presented technique outperform ELM variants for multi-class classification problem. © 2022 ACM.

关键词： Fuzzy set theory

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AGI via Combining Logic with Deep learning 14th

AGI via Combining Logic with Deep Learning

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14th international conference on Artificial General Intelligence (AGI)

作者： Yan, King-Yin Gener Intelligence Hong Kong Peoples R China

ISBN: (纸本)9783030937577;9783030937584

An integration of deep learning and symbolic logic is proposed, based on the Curry-Howard isomorphism and categorical logic. the propositional structure of logic is seen as a symmetry, namely the permutation invariance of propositions;this can be implemented using so-called symmetric neural networks. Under our interpretation, it turns out that Google's BERT, which many currently state-of-the-art language models are derived from, can be regarded as an alternative form of logic. this BERT-like structure can be incorporated under a reinforcement-learning framework to form a minimal AGI architecture. We also mention some insights gleaned from category and topos theory that point to future directions and may be helpful to other researchers, including mathematicians interested in AGI.

关键词： Deep learning Symbolic logic Logic-based AI Neural-symbolic integration Curry-Howard isomorphism Category theory Topos theory Fuzzy logic

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Advances in Bias-aware Recommendation on the Web 21

Advances in Bias-aware Recommendation on the Web

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14th ACM international conference on Web Search and Data Mining (WSDM)

作者： Boratto, Ludovico Marras, Mirko EURECAT Ctr Tecnol Catalunya Barcelona Spain Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781450382977

the goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describing real-world examples of how bias can impact on recommendation algorithms from several perspectives (e.g., ethical and system objectives). the tutorial continues with a systematic showcase of algorithmic countermeasures to uncover, assess, and reduce bias along the recommendation design process. A practical part then provides attendees with implementations of pre-, in-, and post-processing bias mitigation algorithms, leveraging open-source tools and public datasets;in this part, tutorial participants are engaged in the design of bias countermeasures and in articulating impacts on stakeholders. We conclude the tutorial by analyzing emerging open issues and future directions in this rapidly evolving research area (Website: https://***/wsdm2021).

关键词： Bias Discrimination Fairness Recommender Systems Personalized Rankings Collaborative Filtering Machine learning

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Machine learning for Cloud-Based DDoS Attack Detection: A Comprehensive algorithmic Evaluation

Machine Learning for Cloud-Based DDoS Attack Detection: A Co...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Anukriti Naithani Shailendra Narayan Singh Krishna Kant Singh Sushil Kumar Computer Science and Engineering ASET Amity University Noida India Computer Science and Engineering NIT Kurukshetra India

Distributed denial of service attacks, in the recent times, have become a critical threat to not only private server connections but also meta scale cloud computing environments. this research paper furnishes a detailed analysis of five machine learning algorithms in context with network intrusion detection that are; Isolation Forest, Random Forest, Support Vector Machine (SVM) and Artificial Neural Networks (ANN). the dataset used in this study, to differentiate between attack and normal cases, is the KDD cup 1999, wherein the dataset has been converted into binary classes to differentiate between the attack and normal cases. Following are the part of the research; Preprocessing the data, Scaling the features, Training the model and evaluating the performance using crucial metrics like accuracy, precision and F1 score and recall. Graphical representation and confusion matrices for data visualization have been made use of in addition to the aforementioned. the results show a penetrative information about the relative effectiveness and success of different ML algorithms in identifying network intrusions and hence can be used as insightful references for academics and practitioners in the field of network security. A well- explained algorithm selection, based on performance indicators enables an advanced approach to the study, thus providing a framework for further developments in systems of intrusion detection.

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A developed Plan-Do-Check-Act and contextual teaching and learning model

A developed Plan-Do-Check-Act and contextual teaching and le...

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14th international conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2024)

作者： Jun Hu Chaoju Tang Department of Foreign Languages University of Electronic Science and Technology Chengdu People's Republic of China

ISBN: (数字)9781837241958

Despite some application of Contextual Teaching and learning (CTL) theories in practice, their effectiveness has not been prominent, necessitating comprehensive optimization. To enhance the quality and the success of typical processes like education activities, this study integrates CTL with Deming's PDCA (Plan-Do-Check-Act) cycle theory. through the stages of planning, implementation, and evaluation, the CTL model has been significantly improved. Methodologically, the study employs an inductive approach and experimental methods for data analysis. Results show significant improvements in the mentioned activities and enhances of performance and satisfaction of stakeholders like the population involved with the optimized PDCA & CTL model. the results of this paper also confirmed the efficacy of the proposed method in process quality improvement .

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Kernel Extreme learning Machine with Mixture Correntropy for Face Recognition 2022

Kernel Extreme Learning Machine with Mixture Correntropy for...

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14th international conference on Contemporary Computing, IC3 2022

作者： Ahuja, Bhawna Vishwakarma, Virendra P. University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Delhi India

ISBN: (纸本)9781450396752

In this article, a robust kernel extreme learning machine (KELM) framework is designed using mixture correntropy for recognition of facial images. KELM is augmentation of ELM with kernel learning concept, has attained excellent performance in acknowledging numerous classification and regression problems. Due to random projection technique and no requisite of number of hidden neurons specified beforehand, KELM achieves better generalization performance than ELM. Since KELM is designed on MSE paradigm for Gaussian theory of noise, its efficiency may decrease for non-Gaussian cases. To enhance the learning speed and robustness of KELM, this work develops a new KELM framework with mixture correntropy (KELM-MXC) that opts mixture correntropy (MXC) as the optimization paradigm, instead of MSE. Experiments on face databases are performed to prove the effectiveness and comparison analogy with related state-of-the-art algorithms are reported to demonstrate the performance excellence of the presented algorithm. © 2022 ACM.

关键词： Mixtures

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Predictive Analytics in Higher Education: A Systematic Literature Review of Machine learning Models for Forecasting Academic Performance and Ethical Integration

Predictive Analytics in Higher Education: A Systematic Liter...

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international conference on Educational and Information Technology (ICEIT)

作者： Alex N. Remegio College of Engineering Sultan Kudarat State University Isulan Mindanao Philippines

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

this systematic literature review examines predictive analytics and machine learning (ML) applications in edu-cational settings from 2019 to 2023. Machine learning (ML) algorithms are increasingly used to predict students' academic performance and are integral to early warning systems in educational settings. the study looks at several algorithms, from simple linear regression to more complex ensemble and neural network models. It also looks at how well they work in different types of schools and with different types of students. the analysis confirms that ML models provide educators with actionable insights to identify and support at-risk students. Along with evaluating predictive performance, the review discusses the ethical problems of using machine learning in education. these include worries about data privacy, the need for openness, and the possibility of bias in algorithmic decision-making. the paper concludes that while ML holds substantial promise in enhancing educational outcomes, its deployment requires prudent management to navigate the intersection of technological innovation and ethical practice, ensuring responsible use of data analytics in shaping the future of education.

关键词： Ethics Machine learning algorithms Education Neural networks Machine learning Predictive models Prediction algorithms Forecasting Predictive analytics Systematic literature review

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14th international conference on Artificial General Intelligence, AGI 2021

14th International Conference on Artificial General Intellig...

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14th international conference on Artificial General Intelligence, AGI 2021

ISBN: (纸本)9783030937577

the proceedings contain 36 papers. the special focus in this conference is on Artificial General Intelligence. the topics include: Goal Generation and Management in NARS;neuro-Symbolic Architecture for Experiential learning in Discrete and Functional Environments;Navigating Conceptual Space;A New Take on AGI;Categorical Artificial Intelligence: the Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI;moral Philosophy of Artificial General Intelligence: Agency and Responsibility;the Piagetian Modeler;epistolution: How a Systems View of Biology May Explain General Intelligence;measures of Intelligence, Perception and Intelligent Agents;Univalent Foundations of AGI are (not) All You Need;AGI Brain II: the Upgraded Version with Increased Versatility Index;biological Intelligence Considered in Terms of Physical Structures and Phenomena;adaptive Multi-strategy Market Making Agent;unsupervised Context-Driven Question Answering Based on Link Grammar;a Virtual Actor Behavior Model Based on Emotional Biologically Inspired Cognitive Architecture;causal Generalization in Autonomous learning Controllers;AI Future: From Internal Vectors to Simple Objects States Subspaces Maps;a thousand Brains and a Million theories;the Role of Bio-Inspired Modularity in General learning;the Ecosystem Path to AGI;on Comparative Analysis of Rule-Based Cognitive Architectures;elements of Task theory;20NAR1 - An Alternative NARS Implementation Design;the Gap Between Intelligence and Mind;neural String Diagrams: A Universal Modelling Language for Categorical Deep learning;AGI Control theory;AGI via Combining Logic with Deep learning;case-Based Task Generalization in Model-Based Reinforcement learning;pySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture;symbol Emergence and the Solutions to Any Task;compression, the Fermi Paradox and Artificial Super-Intelligence;the Artificial Scientist: Logicist, Emergentist, and Universalist Approaches t

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