Pi Museum: an Educational Escape Room for Learning Mathematics by Irene Araújo, Sónia Pais, Andreia Hall

“… This paper presents the implementation of a virtual educational escape room (EER) titled “Pi Museum”, designed to promote interactive mathematics learning within the context of the International Day of Mathematics, celebrated on March 14th, also known as Pi Day. …”
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Mechanisms of mistrust: A Bayesian account of misinformation learning. by Lion Schulz, Yannick Streicher, Eric Schulz, Rahul Bhui, Peter Dayan

“…From the intimate realm of personal interactions to the sprawling arena of political discourse, discerning the trustworthy from the dubious is crucial. Here, we present a novel behavioral task and accompanying Bayesian models that allow us to study key aspects of this learning process in a tightly controlled setting. …”
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Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

“…Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. …”
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Intercept Guidance of Maneuvering Targets with Deep Reinforcement Learning by Zhe Hu, Liang Xiao, Jun Guan, Wenjun Yi, Hongqiao Yin

“…In this paper, a novel guidance law based on a reinforcement learning (RL) algorithm is presented to deal with the maneuvering target interception problem using a deep deterministic policy gradient descent neural network. …”
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Framework for Addressing Imbalanced Data in Aviation with Federated Learning by Igor Kabashkin

“…This paper presents a novel framework for addressing imbalanced data challenges in aviation through federated learning, focusing on fault detection, predictive maintenance, and safety management. …”
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AI IS LEARNING HOW TO WRITE. ETHICAL PROBLEMS FOR JOURNALISM by Dan Valeriu VOINEA

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Survey on vertical federated learning: algorithm, privacy and security by Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG

“…Federated learning （FL） is a distributed machine learning technology that enables joint construction of machine learning models by transmitting intermediate results （e.g., model parameters, parameter gradients, embedding representation, etc.） applied to data distributed across various institutions.FL reduces the risk of privacy leakage, since raw data is not allowed to leave the institution.According to the difference in data distribution between institutions, FL is usually divided into horizontal federated learning （HFL）, vertical federated learning （VFL）, and federal transfer learning （TFL）.VFL is suitable for scenarios where institutions have the same sample space but different feature spaces and is widely used in fields such as medical diagnosis, financial and security of VFL.Although VFL performs well in real-world applications, it still faces many privacy and security challenges.To the best of our knowledge, no comprehensive survey has been conducted on privacy and security methods.The existing VFL was analyzed from four perspectives: the basic framework, communication mechanism, alignment mechanism, and label processing mechanism.Then the privacy and security risks faced by VFL and the related defense methods were introduced and analyzed.Additionally, the common data sets and indicators suitable for VFL and platform framework were presented.Considering the existing challenges and problems, the future direction and development trend of VFL were outlined, to provide a reference for the theoretical research of building an efficient, robust and safe VFL.…”
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Knowledge, Learning and Transdisciplinary Communication in the Evolution of the Contemporary World by Rita Micarelli, Giorgio Pizziolo

“…All of this can be concretely evidenced by many different experiences already underway or possible in contemporary reality from which emerge both extraordinary evolutionary potentials and serious contradictions and difficulties, as we can summarise through the examples we present. Let us now begin the complex unfolding of the themes announced here in theatrical order and manner.…”
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Detection and Prevention of Medical Fraud using Machine Learning by Ceyda Ünal, Gökçe Sinem Erbuğa

“…Presently, there is an upward trend in the mean life expectancy of individuals due to reductions in maternal and infant mortality, as well as deaths caused by noncommunicable diseases like cardiovascular disease. …”
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Software implementation of the system for learning to write Chinese characters by N. V. Gubanov

“…The Chinese language learning app with character recognition module can help you replace a native speaker or home teacher for self-study. …”
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The effect of digital citizenship on the quality learning civic education by Riza Alrakhman, Dasim Budimansyah, Sapriya Sapriya, Rahmat Rahmat

“…The paper emphasizes the need for empirical evidence or authoritative references to substantiate this assertion, thereby highlighting the crucial role of universities in this context. The study then presents statistical evidence indicating a significant relationship between digital citizenship and learning quality in civic education, as highlighted by a t count (14,510) surpassing the t table (1.66) with a significance level of 0.000, lower than the 0.05 threshold. …”
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The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach by Alisha Lakra, Shubhkirti Gupta, Ravi Ranjan, Sushanta Tripathy, Deepak Singhal

“…The production of goods needs to be accurate and rapid. Thus, for the present research, we have incorporated machine-learning (ML) technology in the manufacturing sector (MS). …”
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Causality, Machine Learning, and Feature Selection: A Survey by Asmae Lamsaf, Rui Carrilho, João C. Neves, Hugo Proença

“…The models are more robust and accurate with the integration of causal reasoning into machine learning, improving applications like prediction and classification. …”
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MLPro 2.0 - Online machine learning in Python by Detlef Arend, Laxmikant Shrikant Baheti, Steve Yuwono, Syamraj Purushamparambil Satheesh Kumar, Andreas Schwung

“…In this paper, we present version 2.0 of the open-source middleware MLPro for applied machine learning in Python. …”
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Adjacent Inputs With Different Labels and Hardness in Supervised Learning by Sebastian A. Grillo, Julio Cesar Mello Roman, Jorge Daniel Mello-Roman, Jose Luis Vazquez Noguera, Miguel Garcia-Torres, Federico Divina, Pedro Esteban Gardel Sotomayor

“…An important aspect of the design of effective machine learning algorithms is the complexity analysis of classification problems. …”
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Review of Masked Face Recognition Based on Deep Learning by Bilal Saoud, Abdul Hakim H. M. Mohamed, Ibraheem Shayea, Ayman A. El-Saleh, Abdulaziz Alashbi

“…As a contribution, we present a detailed taxonomy of MFR approaches, highlight current challenges, and suggest potential future research directions. …”
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Deep Learning in Archiving Indus Script and Motif Information by Vaishnavi Dixit, Nushrat Hussain, Shubham Basak, Deva Atturu, Debasis Mitra, Ujjwal Bhattacharya

“…This work presents a novel computational system for the automated digitization of image-based data from seals of the ancient Indus Valley Civilization (IVC). …”
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Climate Change Analysis in Malaysia Using Machine Learning by Anishalache Subramanian, Naveen Palanichamy, Kok-Why Ng, Sandhya Aneja

“…Climate change presents significant challenges to ecosystems, economies, and societies globally. …”
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Picking Induced Seismicity with Deep Learning (piSDL) by Janis Heuel, Vincent Maurer, Michael Frietsch, Andreas Rietbrock

“…Applying current published PhaseNet models to induced seismicity data leads to only a few events being detected and trained PhaseNet models are not able to outperform well-established workflows in seismology. Here we present a new seismological data set and trained PhaseNet models for picking induced seismicity with deep-learning (piSDL). …”
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Perspectives on Momentary Engagement and Learning Situated in Classroom Contexts by Ricardo Böheim, Jennifer Symonds

“…We begin by presenting definitional, conceptual, and methodological reflections on the construct of momentary engagement, highlighting how moment-to-moment analyses can deepen our understanding of how engagement unfolds in complex, dynamic learning environments. …”
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