Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning.

Active learning approaches require a lot of time investment in student activities and engagement during the class period, which often leads to incomplete coverage of the course syllabus. Furthermore, it requires significant amount of time for the instructor to design and implement active learning strategies. All these shortcomings are often cited as the common hindrance in adopting student active learning. To address these widely recognized inhibiting factors, we recently implemented a new student active learning approach. This approach is based on the students’ presentation and hence termed as student presentation-based active learning (SPAL). Under this approach, students are given a reading assignment to prepare a PowerPoint presentation on well-defined conceptual topics, questions, or chapter modules. Reading assignments on a topic are administered 1–2 weeks before covering them in the class. This allows reasonable time for the self-comprehension of the suggested material for presentation preparation. Students were expected to rehearse the presentation and be prepared to complete it in the suggested time duration. During each lecture, one group of student would present the assigned topic to the class, and their presentations were graded according to the rubric focusing on the coverage of suggested topics, quality of presentation, and after presentation discussions. Peers and instructor provided feedback about the students’ presentation and unclear concepts. To understand the efficacy of this approach, this approach was implemented in Mechanical Engineering Senior Capstone Project I course in Fall 2016. Here the responses and insights garnered from this practice were presented, and discussion on the advantages and challenges associated with the adaptation of this approach in teaching engineering courses as compared to lecture based classroom education system. This paper can provide useful insights for instructors considering this approach or similar student active teaching approach in their courses.

Machine learning and computational methods can accelerate materials discovery by accurately predicting material properties at low cost. Nevertheless, input data to algorithms and structure model parameters remains a key obstacle. The limitations of conventional battery materials could be overcome by high-entropy materials, a unique class of special valuable materials. The knowledge of designing the crystal structure of high-entropy materials is advancing the design and fabrication of new materials for batteries and supercapacitors, even before chemical synthesis, through the use of learning algorithms and quantum computing. In this review, we first focus on quantum computing and the structure of high-entropy materials, especially high-entropy MXenes. We then discuss how to encode and decode the crystal structure of materials, which is a key factor in creating a database for high-entropy materials. We also discuss how to utilize deep learning algorithms for material discovery prior to synthesis, as well as how to employ these algorithms to identify high-entropy materials suitable for batteries and supercapacitors. Finally, we discuss the potential of new quantum computing and artificial intelligence approaches for determining the structure of high-entropy materials in the energy fields.

Recent developments in the field of Information Technology call for a specific reconsideration of the traditional educational methods and facilitate development of new teaching and learning methods and tools, opening it for a wider scope of concerned persons. Active teaching and learning methods as part of educational methodology are widely used at the universities across the European community, putting the larger responsibility of learning at the hands of the learners themselves. One of the major challenges is effective addressing the learning needs of the new generation of students. Postgraduate education in in the field of logistics and supply chain management (LSCM) must keep it in mind in order to promote a suitable answer to the LSCM labor market needs. This is accomplished well by a master programme in LSCM with a European dimension emerged from the synergy of international cooperation between Academia and the professional bodies. This paper shares the empirical study on implementation of Master study programme of Logistics and Supply Chain Management in the Riga Technical University and extensive use of active and innovative learning approach.

Metal-organic framework portable chemical sensor

Active learning is one form of supervised machine learning. In supervised learning, a set of labeled samples is passed to a learning algorithm for training a classifier. However, labeling large amounts of training samples can be costly and error-prone. Active learning deals with the development of algorithms that interactively select a subset of the available unlabeled samples for labeling, and aims at minimizing the labeling effort while maintaining classification performance. The key challenge for the development of so-called active learning strategies is the balance between exploitation and exploration: On the one hand, the estimated decision boundary needs to be refined in feature space regions where it has already been established, while, on the other hand, the feature space needs to be scanned carefully for unexpected class distributions. In this thesis, two approaches to active learning are presented that consider these two aspects in a novel way. In order to lay the foundations for the first one, it is proposed to express the uncertainty in class prediction of a classifier at a test point in terms of a second-order distribution. The mean of this distribution corresponds to the common estimate of the posterior class probabilities and thus is related to the distance of the test point to the decision boundary, whereas the spread of the distribution indicates the degree of exploration in the corresponding region of feature space. This allows for the evaluation of the utility of labeling a yet unlabeled point with respect to classifier improvement in a principled way and leads to a completely novel approach to active learning. The proposed strategy is then implemented and evaluated based on kernel density classification. The generic active learning strategy can be combined with any other classifier, but it performs best if the derived second-order distributions are sufficiently good approximations to the sampling distribution. Although second-order distributions for random forests are derived in this thesis, they do not approximate sufficiently well the sampling distribution and mainly allow only for the relative comparison of prediction uncertainty between test points. In order to combine the state of the art classification performance of random forests with the principal ideas of the first active learning approach, a related second approach for random forests is derived. It is, in addition, tailored to the demands in industrial optical inspection: bag-wise labeling with weak labels and strongly imbalanced classes. Moreover, an outlier detection scheme based on random forests is derived that is used by the proposed active learning algorithm. Finally, a new computational scheme for Gaussian process classification is presented. It is compared to two standard methods in geostatistics, both with respect to theoretical consistency and practical performance. The method evolved as a by-product when considering using Gaussian process models for active learning.

In health education, active learning approaches have gained increasing importance for enhancing the quality of learning, clinical reasoning processes, and socio-emotional skills. The “Cogniverse” is a novel, interactive, and low-cost pedagogical tool that promotes multimodal learning through verbal, associative, and nonverbal communication. This study describes and analyzes the implementation of the “Cogniverse” in health education as a strategy to improve active learning, teamwork, and integrative cognitive engagement. Grounded in social constructivism and Bloom’s Revised Taxonomy, the method focuses on developing higher-order cognitive and socio-emotional competencies through three consecutive stages—verbal explanation, one-word clue, and nonverbal representation—each targeting distinct dimensions of cognitive representation and meaning. The organized use of this activity fosters semantic activation, concept formation, embodied cognition, and collaborative learning, ultimately enhancing memory retention. The findings indicate that engaging in active, play-based learning stimulates motivation, empathy, and clinical reasoning among students and faculty members. Overall, “The Cogniverse” represents a successful, user-friendly, and humanized active learning technique that promotes interdisciplinary learning, communication skill development, and professional competencies aligned with patient-centered care.

This article reviews the recent progress on predicting the adsorption properties of metal-organic framework by using classical density functional theory and focused on the application of the classical density functional theory to the high-throughput screening, which is accelerated by fast Fourier Transform. Comparing to the conventional molecular simulations, the advantage of the accelerated classical density functional theory is the calculation speed, especially for simple small molecule systems, which makes the high-throughput screening on MOF materials feasible. However, it appears that there is a lack of efficient method to deal with the complicated molecules. How to construct a reasonable free energy functional of complicated fluid is the main challenge to state of art classical density functional theory. In a word, the improvement of CDFT theory and the combination of CDFT and molecular simulation are the two main ways for CDFT to predict gas adsorption in MOF.

Active and machine learning-based approaches to rapidly enhance microbial chemical production

Active Learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques.

Staying Active

We compare the level of accuracy achieved by active learning and domain adaptation approaches for quality of transmission estimation of an unestablished lightpath, in presence of small-sized training datasets.

Abstract: Properly designed active learning instruction using “Hands-on” activities could result in higher student motivation and also higher teacher involvement, particularly when it implies a change from less active learning strategies (lectures, presentations) that are more commonly found in the daily life of students and teachers. Often, the expected learning improvement after these changes in instructional strategies, are assessed by standardized tests that usually assess only the declarative knowledge of the students. Occasionally, the impact of these proposals is measured by means of perception of the students. These ways of assessing fail to address the question of what really students learn in Hands-on sessions and if they really learn “better” or “more” using this kind of approach. Actually, recent research suggest that not every active learning session could promote improvements in learning, particularly in terms of conceptual knowledge and deep understanding. The workshop describes in this paper aims to explore an efficient active learning approach for equilibrium and stability concepts. The activities proposed in this workshop show different ways in which these topics can be addressed in an active way with potentially very different learning results. After analyzing this experience, participants are asked to design another learning activity using approaches that effectively promote the expected learning. Designs and their analysis allow the comprehension of frameworks as Understanding by design or Didactical Engineering for the development of effective active learning sessions. Key words: active learning, engineering education, didactical engineering, problem-based learning, hands-on

In recent years, scholars and educators of the English language have shown a lot of interest in the active learning approach. Although a few studies have been conducted on teachers' perceptions and practices of active learning in classrooms, few studies have been conducted in Vietnamese contexts on teachers' perceptions and practices of the function of active learning in reading classrooms. Due to that gap, the current study was conducted to investigate teachers' perceptions regarding active learning techniques used in reading classrooms and the role active learning plays in reading instructions. The paper conducted a descriptive study in order to identify the active learning approaches employed by EFL teachers in reading classrooms at private language centers and investigates their perspectives on the role of active learning in reading instruction. The questionnaire and semi-structured interviews were used to collect data from two sources. The participants were twenty-six teachers of English as a foreign language (EFL) from the province of Vinh Long. The result reveals that the participants had a favorable view of the role of active learning. In particular, they felt that active learning allows students to improve their critical thinking and reading comprehension, as well as teamwork, interpersonal skills, and problem-solving abilities. These findings have a number of educational implications.

This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine their predictions effectively. Experimental results demonstrate that while the MLP initially outperformed RF with an accuracy of 95.83% compared to 91%, the incorporation of active learning significantly improved RF's performance to 94%, narrowing the performance gap between the two models. After applying active learning, both classifiers showed enhanced precision, recall, and F1-scores, with improvements ranging from 3% to 5%. This study provides valuable insights into the role of active learning in boosting the efficiency of machine learning models for dynamic spoofing scenarios in ASV systems. Future research should focus on designing advanced active learning techniques and exploring their integration with other machine learning paradigms to further enhance ASV security.

Deep neural network has been extensively applied to hyperspectral image (HSI) classification recently. However, its success is greatly attributed to numerous labeled samples, whose acquisition costs a large amount of time and money. In order to improve the classification performance while reducing the labeling cost, this article presents an active deep learning approach for HSI classification, which integrates both active learning and deep learning into a unified framework. First, we train a convolutional neural network (CNN) with a limited number of labeled pixels. Next, we actively select the most informative pixels from the candidate pool for labeling. Then, the CNN is fine-tuned with the new training set constructed by incorporating the newly labeled pixels. This step together with the previous step is iteratively conducted. Finally, Markov random field (MRF) is utilized to enforce class label smoothness to further boost the classification performance. Compared with the other state-of-the-art traditional and deep learning-based HSI classification methods, our proposed approach achieves better performance on three benchmark HSI data sets with significantly fewer labeled samples.