Objective: Evaluate the effectiveness of distance learning courses for the purpose of interdisciplinary continuing education in Speech Pathology and Dentistry. Methods: The online course was made available on the Moodle platform. A total of 30 undergraduates participated in the study (15 from the Dentistry course and 15 from the Speech Pathology course). Their knowledge was evaluated before and after the course, in addition to the user satisfaction by means of specific questionnaires. The course was evaluated by 6 specialists on the following aspects: presentation and quality of the content, audio-visual quality, adequacy to the target public, and information made available. To compare the obtained results in the pre- and post-course questionnaires, the test Wilcoxon was carried out, with a 5% significance level. Results: the teaching/learning process, including the theoretical/practical application for the interdisciplinary training, proved to be effective as there was a statistically significant difference between the pre- and post- course evaluations (p<0.001), the users’ satisfaction degree was favorable and the specialists evaluated the material as adequate regarding the target public, the audio-visual information quality and the strategies of content availability. Conclusion: The suggested distance-learning course proved to be effective for the purpose of Speech Pathology and Dentistry interdisciplinary education.

Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open University of Portugal dictates. This article analyses the behavior of the students of the 1st cycle in Computer Science while interacting with the object-oriented programming (OOP) discipline available to them on the Moodle platform. Through the evaluation of the level of interaction achieved in a group of relevant selected actions by the students, it is possible to identify their relevancy to the success of the programming learning process. Data was extracted from Moodle, numerically analyzed, and, with the use of some charts, behavior patterns of students were identified. This paper points out potential new approaches to be considered in e-learning in order to enhance programming learning results, besides confirming a high level of drop-out and a low level of interaction, thus finding no clear correlation between students’ success and the number of online actions (especially in forums), which reveals a possible failure of the main pillar on which the e-learning model relies.

This paper reports on a case study of a group of six non-native English speaking migrant women’s experiences learning English vocabulary in a mobile assisted language learning (MALL) environment at a small community centre in Western Australia. A sociocultural approach to learning vocabulary was adopted in designing the MALL lessons that the women undertook. The women provided demographic information, responded to questions in a pre-MALL semi-structured interview, attended the MALL lessons, and completed a post-MALL semi-structured interview. This study explores the sociocultural factors that affect migrant women’s language learning in general, and vocabulary in particular. The women’s responses to MALL lessons and using the tablet reveal a positive effect in their vocabulary learning.

This paper provides an overview of the impact that social media has on the development of collaborative learning within a cohort environment in a doctoral program. The researchers surveyed doctoral students in an education program to determine how social media use has influenced the doctoral students. The study looked at the following areas: a) the ability of social media use to develop a collaborative learning environment, b) access to social media content which supports learning, and c) whether social media use has contributed to the enhancement of the doctoral students’ academic achievement and learning progress. As social media use and on-line learning become more prevalent in education, it is important to continue to understand the impact that social media has on improving students’ ability to achieve their academic goals. This study provides insight on how doctoral students used social media and how social media use has influenced academic development in their cohort environment. In addition, this paper provides a discerning view into the role social media plays when developing a collaborative learning environment in a cohort.

Aim/Purpose: To develop a framework for utilizing Mobile Assisted Language Learning (MALL) to assist non-native English migrant women to acquire English vocabulary in a non-formal learning setting. Background: The women in this study migrated to Australia with varied backgrounds including voluntary or forced migration, very low to high levels of their first language (L1), low proficiency in English, and isolated fulltime stay-at-home mothers. Methodology: A case study method using semi-structured interviews and observations was used. Six migrant women learners attended a minimum of five non-MALL sessions and three participants continued on and attended a minimum of five MALL sessions. Participants were interviewed pre- and post-sessions. Data were analysed thematically. Contribution: The MALL framework is capable of enriching migrant women’s learning experience and vocabulary acquisition. Findings: Vocabulary acquisition occurred in women from both non-MALL and MALL environment; however, the MALL environment provided significantly enriched vocabulary learning experience. Future Research: A standardised approach to measure the effectiveness of MALL for vocabulary acquisition among migrant women in non-formal setting

Aim/Purpose: This study examines the influence of Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC) on the use of smart phones for mobile learning by postgraduate students in University of Ibadan, Nigeria. Background: Due to the low level of mobile learning adoption by students in Nigeria, three base constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model were used as factors to determine smart phone use for mobile learning by the postgraduate students in the University of Ibadan. Methodology: The study adopted a descriptive survey research design of the correlational type, the two-stage random sampling technique was used to select a sample size of 217 respondents, and a questionnaire was used to collect data. Descriptive statistics (frequency counts, percentages, mean, and standard deviation), test of norm, and inferential statistics (correlation and regression analysis) were used to analyze the data collected. Contribution: The study empirically validated the UTAUT model as a model useful in predicting smart phone use for mobile learning by postgraduate students in developing countries. Findings: The study revealed that a significant number of postgraduate students used their smart phones for mobile learning on a weekly basis. Findings also revealed a moderate level of Performance Expectancy (???? =16.97), Effort Expectancy (???? =12.57) and Facilitating Conditions (???? =15.39) towards the use of smart phones for mobile learning. Results showed a significant positive relationship between all the independent variables and use of smart phones for mobile learning (PE, r=.527*; EE, r=.724*; and FCs, r=.514*). Out of the independent variables, PE was the strongest predictor of smart phone use for mobile learning (β =.189). Recommendations for Practitioners: Librarians in the university library should organize periodic workshops for postgraduate students in order to expose them to the various ways of using their smart phones to access electronic databases. Recommendation for Researchers: There is a need for extensive studies on the factors influencing mobile technologies adoption and use in learning in developing countries. Impact on Society: Nowadays, mobile learning is increasingly being adopted over conventional learning systems due to its numerous benefits. Thus, this study provides an insight into the issues influencing the use of smart phones for mobile learning by postgraduate students from developing countries. Future Research: This study utilized the base constructs of the UTAUT model to determine smart phone use for mobile learning by postgraduate students in a Nigerian university. Subsequent research should focus on other theories to ascertain factors influencing Information Technology adoption and usage by students in developing countries.

Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This paper compares adaptive learning with a conventional teaching approach implemented in a digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized.

Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

Carlo Lipizzi’s Societal impacts of artificial intelligence and machine learning offers a critical and comprehensive analysis of artificial intelligence (AI) and machine learning’s effects on society. This book provides a balanced perspective, cutting through the

You probably already know the benefits of learning a language, so let’s focus on the app. Right off the bat, let’s be clear about one thing: When we say “app” we don’t mean that you’re limited to using Babbel on your phone. You can use Babbel on desktop, too, and your progress is synchronized across […]

What should employers and environmental, health and safety professionals consider when choosing virtual training for workers?

Oxipital AI’s 3D vision and AI offerings aim to be more convenient and effective through a different method of “training” its products.

An internship with Arm gives you exposure to real work and insight into the Arm innovations that shape extraordinary. Students who thrive at Arm take their love of learning beyond their experience of formal education and develop new ideas. This is the energy that interests us.

Internships at Arm will give you the opportunity to put theory into practice through exciting, intellectually challenging, real-world projects that enrich your personal and technical development while enhancing your future career opportunities.

This internship position is within Machine Learning Group in Arm which works on key technologies for the future of computing. Working on the cutting edge of Arm IP, this Group creates technology that powers the next generation of mobile apps, portable devices, home automation, smart cities, self-driving cars, and much more.

When applying, please make sure to include your most up to date academic transcript.

For a sneak peek what it’s like to work in Arm Lund, please have a look at the following video: http://bit.ly/2kxWMXp

The Role

You will work alongside experienced engineers within one of the IP development teams in Arm and be given real project tasks and will be supported by experienced engineers. Examples of previous project tasks are:

• Developing and trialing new processes for use by the design/verification teams.
• Investigating alternative options for existing design or verification implementations.
• Help to develop a hardware platform that can guide out customers to the best solution.
• Implement complex logic using Verilog to bridge a gap in a system.
• Develop bare metal software to exercise design functionality.
• Verify a complex design, from unit to full SoC level.
• Help to take a platform to silicon.

Arm's Machine Learning Group is seeking for a highly motivated and creative Graduate Software Engineer to join the Cambridge-based applied ML team.

From research, to proof-of-concept development, to deployment on ARM IPs, joining this team, would be a phenomenal opportunity to contribute to the full life-cycle of machine learning projects and understand how state-of-the-art machine learning is used to solve real word problems.

Working closely with field experts in a truly multi-discipline environment, you will have the chance to explore existing or build new machine learning techniques, while helping unpick the complex world of use-cases that are applied on high end mobile phones, TVs, and laptops.

About the role

Your role would be to understand, develope and implement these use case, collaborating with Arm's system architects, and working with our marketing groups to ensure multiple Arm products are molded to work well for machine learning. Also, experience deploying inference in a mobile or embedded environment would be ideal. Knowledge of the theory and concepts involved in ML is also needed, so fair comparisons of different approaches can be made.

As an in depth technical role, you will need to understand the complex applications you analyse in detail and communicate them in their simplest form to help include them in product designs, where you will be able to influence both IP and system architecture.

The post Machine Learning Technology in Predicting Relapse and Implementing Peer Recovery Intervention Before Drug Use Occurs was curated by information for practice.

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo.

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment solutions are not always available. In this study, a novel approach that combines optimisers with neural network surrogate models to significantly reduce the alignment overhead for a mobile soft X-ray spectrometer is proposed. Neural networks were trained exclusively using simulated ray-tracing data, and the disparity between experiment and simulation was obtained through parameter optimization. Real-time validation of this process was performed using experimental data collected at the beamline. The results demonstrate the ability to reduce alignment time from one hour to approximately five minutes. This method can also be generalized beyond spectrometers, for example, towards the alignment of optical elements at beamlines, making it applicable to a broad spectrum of research facilities.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Each year, tens of millions of Americans, young and old, choose to learn about science in informal ways -- by visiting museums and aquariums, attending after-school programs, pursuing personal hobbies, and watching TV documentaries, for example.

A shift is needed in how science, technology, engineering, and mathematics (STEM) subjects are taught to students in grades K-12 who are learning English, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Scientific research that involves nonscientists contributing to research processes – also known as ‘citizen science’ – supports participants’ learning, engages the public in science, contributes to community scientific literacy, and can serve as a valuable tool to facilitate larger scale research, says a new report from the National Academies of Sciences, Engineering, and Medicine.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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I’ll admit it. I was skeptical that distance learning could be a worthwhile experience for service contractors.