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Proper Classroom Management remains challenging for teachers with problems such as inappropriate student behaviour resulting in chaotic classrooms, shouting, tantrums, teacher disrespect, stealing food, cash, etc. Therefore, the researchers conducted a study to determine the effectiveness of teachers' classroom management skills at San Gabriel Elementary School Academic Year 2021- 2022: Towards a Guide.    The researchers used a simple random sampling technique. San Gabriel Elementary School Teachers were chosen through drawing lots to have an equal chance to be study respondents. A descriptive method design was applied in this study in which data were collected quantitatively and analyzed using qualitative procedures. Thirty (30) teacher respondents employed different classroom management styles.

[Abstract]: Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them.

[Abstract]: Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to actively involve experts into the learning loop of machine learning (ML) models. One of these techniques is Machine Teaching (MT) which tries to apply techniques that come from the world of didactics within machine learning (ML), such as sorting the dataset according to its difficulty and presenting the cases to the model in incremental levels of complexity. In this work we propose a new twist to MT: since its foundation is to bring didactic techniques to ML, why not use this technique as a didactic method itself? In this case we propose the creation of an ML model for the identification of diphthongs and hiatuses in the Spanish language. The first step is to develop a deep learning model to identify diphthongs and hiatuses using Curriculum Learning (CL) and a sorted dataset that identifies simple and complex cases. The accuracy of this model identifies the upper limit of efficiency that we can obtain by training the model. The next step is to reset the weights of the model but retain its architecture and offer the model to the students for its training. The idea is that students use MT techniques to make the model learn again, but the ultimate goal is that students learn by teaching in an informal and gamified learning environment. The results show how a HITL strategy can make a model learn iteratively to identify diphthongs and hiatuses and a workflow is proposed to include this technique in the classroom.

Contains fulltext : 294546.pdf (Publisher’s version ) (Open Access), The objectives of the 1st EFORT European Consensus on 'Medical and Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices' were foremost to focus on patient safety by establishing performance requirements for medical devices. The 1st EFORT European Consensus applied an a priori-defined, modified Delphi methodology to produce unbiased, high-quality recommendation statements, confirmed by consensus voting of a European expert panel. Intended key outcomes are practical guidelines justified by the current stage of knowledge and based on a broad European Expert Consensus, to maintain innovation and optimisation of orthopaedic devices within the boundaries of MDR 2017/745. Twenty-one main research areas of relevance were defined relying on input from the EFORT IPSI WG1 'Introduction of Innovation' recommendations and a related survey. A modified Delphi approach with a preparatory literature review and work in small groups were used to prepare answers to the research questions in the form of 32 draft Consensus statements. A Consensus Conference in a hybrid format, on-site in the Carl Gustav Carus University of Dresden was organised to further refine the draft statements and define consensus within the complete group of participants by final voting, intended to further quantify expert opinion knowledge. The modified Delphi approach provides practical guidelines for hands-on orientation for orthopaedic surgeons, research institutes and laboratories, orthopaedic device manufacturers, patient representatives, Notified Bodies, National Institutes and authorities. For the first time, initiated by the EFORT IPSI (WG1 'Introduction of Innovation'), knowledge of all related stakeholders was combined in the 1st EFORT European Consensus to develop guidelines and result in a comprehensive set of recommendations.

This opening chapter provides some insights on our understanding insofar as our understanding on what constitutes journalism in the Global South is concerned. We critique this focus area of journalism research by identifying the challenges and opportunities it presents. We also look at key concepts and debates dominating studies and developments in journalism scholarship in non-Western settings. We also identify areas we think will be of interest for practitioners and researchers interested in studying journalism in the Global South. Finally, we identify reasons why we think this companion is important and why specifically now.

[Abstract]: The current context of Quantum Computing and its available technologies present an extensive variety of tools and lack of methodologies, leading to incompatibilities across platforms, which end up as inconsistencies in the developed solutions. We propose a design called Quantum Factory Method, based on software engineering and design patterns, to solve these issues by integrating different quantum platforms in the same development. We provide example implementations whose results prove the suitability of the design in different cases, and conclude on how this approach can be expanded for future work.

[Abstract]: Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.

[Abstract] The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models on a diagnosis scenario with the goal to decide whether a patient should be prescribed with a chemotherapy treatment. To validate the diagnosis results we explore different explainability approaches: Decision Tree, Random Forest, and model agnostic ad-hoc models, and compare them against a standard Pancreatic Cancer treatment set of rules. The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models. To validate the diagnostic results we explore different explainability approaches: Decision Tree based approach, Random Forest based approach, and different model agnostic ad-hoc approaches, and we compare them against a standard Pancreatic Cancer treatment set of rules