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关键词： After school programs   ASD   Autism   Coding   Computer science   Informal learning   Robotics   STEM  

摘要： Research has established that there have been limited opportunities for students with a diagnosis of autism spectrum disorder (ASD) to participate in science, technology, engineering, and mathematics (STEM) learning activities that are designed to meet their diverse needs, particularly activities that are intended to promote computer science literacy. Thus, the purpose of this study was to examine outcomes across time associated with a robotics program for children with ASD while accounting for a variety of relevant variables, including the degree of ASD symptoms, presence of ADHD symptoms, degree of challenging behaviors, quality of relationships, and sensory sensitivity. Participants included 12 elementary students with a diagnosis ASD who were enrolled in a community-based after-school program for students with ASD. Participants engaged in robotics and coding activities across 7 weeks through structured and unstructured exploration. Engagement and diagnostic markers were observed as students participated in robotics and coding tasks. Additionally, measures were obtained for indications of ASD, ADHD, quality of relationships, challenging behaviors, and sensory sensitivity. Data were used to evaluate the association of ASD diagnostic marker and engagement scores across time via the number of sessions. Results show a decrease in participants’ diagnostic ASD markers and an increase in engagement as sessions progressed. Background variables such as presence of ADHD symptoms, challenging behaviors, and sensory sensitivities did not significantly influence outcomes, suggesting that the design of robotics and coding programs for students with ASD need not hinge upon on these factors. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Clustering methods   Noise measurement   Clustering algorithms   Computer science   Self-organizing feature maps   Dimensionality reduction   Classification algorithms   Self organizing map   multiview   clustering   classification   pipeline  

摘要： Clustering has proved to be a successful classification method when it comes to dealing with multiview data. Each method and technique tries to achieve efficiency and accuracy in classifying the multiview data. Multi-source data contains noise and divergence. Another problem is that each view contains many features, so usually the multiview dataset is multi-dimensional. This raises basic problems like the need for a dimensionality reduction technique for optimal selection of features, fusing the data of different views, and maintaining the inter- and intra-consensus of the multiview dataset. The fusion technique should merge the complementary information efficiently. The goal of this study is to use a promising technique for dimension reduction that reduces the noise but maintains the inter-view and cross-view consensus. A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. It calculates the dimension relevance with various data instances. These further place the relevant dimension samples in one group. The method does not need to know the number of clusters before hand, as it dynamically determines it during the process. Finally, this study proposed a novel multi-view learning framework that analyzes multi-source data and generates fine clusters efficiently.

关键词： Telecommunications   Prediction algorithms   Accuracy   Mobile handsets   Computer science   Telephony   Customer satisfaction   Churn rate   Churn   telecommunications   prediction   machine learning   explainability   literature review  

摘要： The telecommunications sector has experienced exponential growth since the year 2000, reaching 5.31 trillion users by 2022, generating 1.07 trillion in revenue for telecommunications companies. However, this growth leads to intense competition among companies for customer acquisition, often resulting in customer churn or switching between telecommunications providers due to the services or experiences received by users. At the moment, algorithms are being developed for churn prediction to assist in taking actions to prevent customer defection. Nevertheless, there is no comprehensive inventory of churn factors or a list of algorithms that explain the reasons for customer churn, despite the existence of numerous factors influencing user retention or defection. This highlights the importance of our work in allowing us to know in advance which client will be a deserter (churn). The purpose of this research is to identify how factors intervene in the customer churn process in telecommunications companies, as well as to list existing techniques for prediction and finally, to present advances in explainability, using a systematic literature review from 2018 to August 2023 using the Scopus and Wos meta-search engines. Regarding the factors contributing to churn, we identified 19 factors encompassing 87 sub-factors, detailed in 87 out of 112 reviewed articles. In terms of prediction, 26 unique algorithm techniques and 16 combinations were identified, presented in 102 out of 112 reviewed articles. Finally, in the realm of explainability, 4 out of 112 articles were found, detailing 5 techniques that help identify why an algorithm selects a customer as potential defector.

关键词： medication adherence   digital technology   digital pharmacy   electronic adherence monitoring   data management   data cleaning   research methodology   algorithms   R   semiautomated   code   coding   computer science   computer programming   medications   computer script  

摘要： Background: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence. Objective: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users. Methods: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients' demographic data. The script formats the data longitudinally and calculates each day's medication implementation. Results: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Delta+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence. Conclusions: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.

关键词： Climate change   Computer science  

摘要： TheNaturePodcastteam have rewritten two popular holiday songs in honour of some of the biggest science stories of in 2023.

关键词： Students  

摘要： The integration of artificial intelligence tools such as ChatGPT in the education system has gained attention in recent years. This experience report explores students’ perceptions and suggestions for integrating ChatGPT in a computer science course. Following a ChatGPT activity which includes code completion and analysis, seven students participated in in-depth interviews. Findings from the transcribed interviews suggest that ChatGPT has the potential to enhance learning experience including programming. They highlighted the tool’s ability to respond immediately to queries and supporting personalised learning. However, they raise concerns that heavy reliance on ChatGPT may adversely affect students’ critical thinking and problem-solving skills. These findings show the importance of carefully balancing using ChatGPT in computer science courses. The findings of this research have significant implications for educators, curriculum designers and policymakers as they explore integrating AI tools into educational contexts. © 2023, CC BY.

关键词： Computational models   Computational science   Computer science   Statistical methods   Translational research  

摘要： Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications. Existing feature visualisation methods are not well-suited for regression tasks. Here, authors introduce a method to learn the manifold topology related to deep neural network output and target labels and provide insightful visualisations of the high-dimensional features while preserving the local geometry.

关键词： Computer science   Machine learning   Mathematics and computing  

摘要： The poster child for generative AI software is a startling human mimic. It represents a potential new era in research, but brings risks.

关键词： Chemistry   Computer science   Machine learning  

摘要： Automation of chemistry research has focused on developing robots to execute jobs. Artificial-intelligence technology has now been used not only to control robots, but also to plan their tasks on the basis of simple human prompts.

关键词： Chemistry   Computer science  

摘要： Transformer-based large language models are making significant strides in various fields, such as natural language processing1-5, biology6,7, chemistry8-10 and computer programming11,12. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research. Coscientist is an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.