Reviewerly/exHarmony huggingface dataset:
Core Metadata Schema for Learner Corpora

This document contains a list of metadata fields that can be used to describe learner corpus data. The core metadata scheme is structured around 8 metadata types: - Administrative metadata; - Corpus design metadata; - Learner; - Text (language sample); - Task; - Annotation; - Annotator; - Transcriber. The last tab is an example of an additional module that could be used to provide info about an interviewer in the case of interviews with L2 learners.
Soft computing techniques for prediction of forest fire occurrence in Brunei Darussalam

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhammad Iskandar Hanafi Bin Pengiran Haji Zahari, Rama Rao Karri, Mohamed Hasnain Isa, El-Said Mamdouh Mahmoud Zahran, S. M. Shiva Nagendra; Soft computing techniques for prediction of forest fire occurrence in Brunei Darussalam. AIP Conference Proceedings 10 January 2023; 2643 (1): 030023. https://doi.org/10.1063/5.0110349 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Deploying Different Lightweight Kubernetes on Raspberry Pi Cluster

The purpose of this paper is to present an implementation of a cluster of 20 Raspberry Pis and different local multi-node deployments of Kubernetes on top of it. The presented approaches differ both in terms of the used lightweight Kubernetes solutions and the used operating systems. Besides the different methods for deploying applications in containers, in the current paper are presented the interactions with the cluster as well as their differences. Based on collected metrics from the Kubernetes nodes are presented and compared loads of the different deployments.
Approaches to Research Data Infrastructure for Archaeological Science

Building databases and research tools for archaeological and environmental data, and maximizing their accessibility for a broad spectrum of potential users, is a non-trivial problem which can be approached in a number of different ways. Different user groups require different points of entry, are interested in different levels of detail, and require different levels of access, filters and visualizations. A single user interface may provide for an efficient, multiproxy approach for large scale syntheses, but may at the same time alienate specialists. This chapter will discuss some of these issues, drawing on experience from a number of international projects at different scales, including 1) The Bugs Coleopteran Ecology Package (BugsCEP), an ecology orientated fossil insect database; 2) The Strategic Environmental Archaeology Database (SEAD), a multiproxy data infrastructure for archaeological science; and 3) DataARC, an inter-archive data discovery tool which links databases through concepts rather than data. The chapter concludes with an extended list of Grand Challenges for research data infrastructure for archaeology to help others navigate this theme.
Secure Data Hiding and Extraction Using RSA Algorithm

Data hiding and extraction is an important aspect of information security because the data will be safer and more manageable. Data hiding technology that works well produces data that is efficient, secure, and simple to connect. The underlying communication network that enables the transport of sensitive data is insecure and unprotected. The fast rise of electronic methods of communication implies that information security has become a critical concern in the real world. As such, anyone with the necessary expertise and software may eavesdrop and intercept data transmissions, which can be extremely harmful and even life threatening in so many cases. Therefore, the research of this paper offers a software framework that allows users to create messages and hide them within image file documents that can be sent to a desired recipient. Moreover, we conducted an experiment with different data that guarantee the increase level of confidentiality of information exchange over the internet.
pyaerso: A Rust-backed Python Module for Accessible Flight Dynamics Modelling for Machine Learning

This work presents an open-source Python module for flight dynamics modelling, with the core numerical operations implemented in Rust for improved performance. The primary use case is as part of machine learning research, where Python is the dominant language. Previous machine learning work by the authors had begun to show the performance limitations of a pure Python flight dynamics model. The module presented here began development in order to accelerate future models. To evaluate the module, a simple model was implemented using it and an equivalent was created for JSBSim. This was then used to benchmark performance and compare interfaces and workflows. For the simple model used, performance of the new module is shown to be comparable to JSBSim, and indeed faster when extracting state at each timestep from a Python wrapper. In future machine learning work, this improved performance will allow more training time for machine learning models, resulting in better trained agents capable of performing more complex manoeuvres.
Neural network interpretation techniques for analysis of histological images of breast abnormalities

Background. Neural networks are actively used in digital pathology to analyze histological images and support medical decision-making. A common approach is to solve the classification problem, where only class labels are the only model responses. However, one should understand which areas of the image have the most significant impact on the model's response. Machine learning interpretation techniques help solve this problem.&#x0D; Aim. To study the consistency of different methods of neural network interpretation when classifying histological images of the breast and to obtain an expert assessment of the results of the evaluated methods.&#x0D; Materials and methods. We performed a preliminary analysis and pre-processing of the existing data set used to train pre-selected neural network models. The existing methods of visualizing the areas of attention of trained models on easy-to-understand data were applied, followed by verification of their correct use. The same neural network models were trained on histological data, and the selected interpretation methods were used to systematize histological images, followed by the evaluation of the results consistency and an expert assessment of the results.&#x0D; Results. In this paper, several methods of interpreting machine learning are studied using two different neural network architectures and a set of histological images of breast abnormalities. Results of ResNet18 and ViT-B-16 models training on a set of histological images on the test sample: accuracy metric 0.89 and 0.89, ROC_AUC metric 0.99 and 0.96, respectively. The results were also evaluated by an expert using the Label Studio tool. For each pair of images, the expert was asked to select the most appropriate answer ("Yes" or "No") to the question: "The highlighted areas generally correspond to the Malignant class." The "Yes" response rate for the ResNet_Malignant category was 0.56; for ViT_Malignant, it was 1.0.&#x0D; Conclusion. Interpretability experiments were conducted with two different architectures: the ResNet18 convolutional network and the ViT-B-16 attention-enhanced network. The results of the trained models were visualized using the GradCAM and Attention Rollout methods, respectively. First, experiments were conducted on a simple-to-interpret dataset to ensure they were used correctly. The methods are then applied to the set of histological images. In easy-to-understand images (cat images), the convolutional network is more consistent with human perception; on the contrary, in histological images of breast cancer, ViT-B-16 provided results much more similar to the expert's perception.
Statistical biopsy: An emerging screening approach for early detection of cancers

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a "statistical biopsy." Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.
Semi-supervised Multi-organ Segmentation with Cross Supervision Using Siamese Network

Numerous unlabeled data is useful for supervised medical image segmentation, if the labeled data is limited. To leverage all the unlabeled images for efficient abdominal organ segmentation, we developed semi-supervised framework with cross supervision using siamese network, i.e., SemiSeg-CSSN. Cross supervision enables the two networks to optimize the network using pseudo-labels generated by the other. Moreover, we applied the cascade strategy for the task because of the large and uncertain locations of the abdomen regions. To validate the effects of unlabeled data, we employed an unlabeled image filtering strategy to select the unlabeled image and their pseudo label images with low uncertainty. On the FLARE2022 validation cases, with the help of unlabeled data, our method obtained the average dice similarity coefficient (DSC) of 77.7% and average normalized surface distance (NSD) of 82.0%, which is better than the supervised method. The average running time is 12.9 s per case in inference phase and maximum used GPU memory is 2052 MB.
Teaching through Flipped Classroom and Learning Communities: Analisys of a Computer Programming Course during Pandemic

Due to the COVID-19 pandemic, education underwent an important change, forcing courses to follow an online modality. This article shows the case study of a Computer Programming course at the National University of Colombia, using the flipped classroom methodology, where students through an LMS could view content and perform exercises, and subsequently had synchronous sessions with teachers. Additionally, learning communities were created through the use of instant messaging that allowed interaction between students with classmates and teachers. The results and reflections obtained with the proposed methodology are presented from the review of the use of tools but also from student progress and perceptions.
Robust image hashing based on quaternion polar complex exponential transform and image energy

To apply the quaternion moments to the image feature extraction process and improve the performance of the image hashing algorithm, we propose a hashing algorithm based on quaternion polar complex exponential transform and image energy. First, the input color image is preprocessed to form a secondary image. The secondary image is subjected to fast quaternion generic polar complex exponential transform to obtain the mixed low-order moment coefficients as the global features of the image. Second, the algorithm extracts energy change features on the edges of the secondary image and energy point features in the center of the image as energy features of the image under the YCbCr color space. Finally, the image’s mixed low-order moment and energy features are combined and scrambled to form the final hash sequence. The experimental results show that the proposed algorithm balances robustness and differentiation well. Moreover, the hash sequence is the shortest, and the algorithm has better check-all and check-accuracy rates compared with other algorithms in the copy detection experiments.
Performance comparison of Dask and Apache Spark on HPC systems for neuroimaging

Summary The general increase in data size and data sharing motivates the adoption of Big Data strategies in several scientific disciplines. However, while several options are available, no particular guidelines exist for selecting a Big Data engine. In this paper, we compare the runtime performance of two popular Big Data engines with Python APIs, Apache Spark, and Dask, in processing neuroimaging pipelines. Our experiments use three synthetic neuroimaging applications to process the 606 GB BigBrain image and an actual pipeline to process data from thousands of anatomical images. We benchmark these applications on a dedicated HPC cluster running the Lustre file system while using varying combinations of the number of nodes, file size, and task duration. Our results show that although there are slight differences between Dask and Spark, the performance of the engines is comparable for data‐intensive applications. However, Spark requires more memory than Dask, which can lead to slower runtime depending on configuration and infrastructure. In general, the limiting factor was the data transfer time. While both engines are suitable for neuroimaging, more efforts need to be put to reduce the data transfer time and the memory footprint of applications.
Reinventing Workplace Learning and Development: Envisaging the Role of AI

Purpose: In the contemporary knowledge economy, organisations mainly derive a competitive advantage by leveraging their intangible assets. Competent and motivated employees are the primary strategic resources to attain innovation and business continuity. Consequently, workplace learning and development (L&D) is at the forefront of the human resource management (HRM) discipline. At the same time, with the changing technology landscape, organisations are transforming their L&D function to be sustainable. Against this backdrop, the main objective of this chapter is to illustrate how artificial intelligence (AI) contributes to a specific HRM sub-function, that is, workplace L&D.Design/Methodology/Approach: Grounded on intense scrutiny of literature, this chapter construes AI as intelligent machines that think and work like humans and have the potential for enhancing learning processes. Different themes have been presented, which suggest the capabilities of AI systems to fuel employee learning at the workplace.Findings: Findings demonstrate that AI-enabled workplace learning is rooted in improved knowledge management (KM) capabilities, developmental feedback, personalised education, learning for a diverse pool of learners, virtual mentoring, and chatbot-based learning.Research Limitations/Implications: This conceptual study suffers from a lack of empirical support.Practical Implications: This chapter contributes to expanding scholarship on integrating AI and the HRM domain, particularly L&D. Further, it highlights how L&D professionals should integrate AI into employee learning journeys to evoke effective learning outcomes.Originality/Value: This chapter provides a gestalt approach to integrating AI with employee L&D
Author Attribution of Literary Texts in Polish by the Sequence Averaging

The paper concerns the authorship recognition in a collection of Polish literary texts from the late 19th and early 20th centuries, consisting of 99 novels from 33 authors. The authors divide the books into smaller parts and analyze the classification based on a book part. To mimic the real task of testing an unknown book, the data set has been divided in such a way that parts of the same book do not appear simultaneously in the training and test set. The authors compare the approaches by working with raw texts, and, to avoid the semantic features of the text, they represent texts in the form of a sequence of grammatical class bigrams. In the case of raw text analysis, classical TF-IDF, supervised fastText, and contemporary transformer-based BERT are analyzed. In the case of grammatical classes, only TF-IDF and fastText are applied. In addition, the authors propose a sequence averaging method that works by dividing the text into smaller parts, classifying each part separately, and making the final classification based on averaging results from each part of the text. The study suggests that the TF-IDF on the raw text outperforms other methods and the sequence averaging improves the classification results for most of the analyzed schemas. Surprisingly, the BERT based method is the worst. This phenomenon is carefully analyzed and explained.
Definition of the Nested Metagraph

The metagraph is a graphical construct that has proven to be very useful for graphical visualizations and quantitative analysis. This advantage determines the most promising area for their application: the problems of big data analysis, provided that this data can be described using a graph representation. The article proposes an approach to modeling hierarchical systems based on nested metagraphs. Category theory is used to eliminate ambiguity in the interpretation of concepts. Formalized descriptions of static and dynamic nested metagraphs, methods for defining them, as well as basic operations are given. The basis of the nested metagraph concepts is a monoid, that is considered as an information object, which is characterized by an internal form, internal and external contents. Thus, a monoid is a generalization of a graph structure and is interpreted as a vertex of a generalized graph.
A Special Toast to Govindjee Govindjee on Celebrating his 90th Birthday on 24 October 2022

We are honoured to provide a very special toast to Govindjee, ‘Mister Photosynthesis’, who celebrated his 90th birthday on 24 October 2022. Here we present 26 messages from those others who have known Prof. Govindjee, but who for various reasons could not submit their posts for publication by Naithani et al. (2022), which highlighted more than 100 messages. In this special toast to Govindjee, we also provide recent photographs of Govindjee (~24 October, 2022).
Teachers' perspectives toward using augmented reality technology in science learning

Augmented reality (AR) is a new technology in education, and teachers are still rarely familiar with the technology. The study aimed to introduce AR technology and determine the perspective of science teachers from various educational backgrounds on implementing AR in science learning. A mixed research design was employed in collecting quantitative and qualitative data using an online survey and one-on-one semi-structured interviews. The study participants are 32 teachers with different educational backgrounds in the Association of Science Education Teachers in West Bandung, Indonesia. The interviews were conducted with six participants selected randomly. The study found that many teachers had less knowledge of AR technology because they had experienced it for the first time. However, they were highly interested in implementing the technology in science learning after exploring its use independently. From the perspective of teachers, AR has the potential to be implemented in science learning. It could facilitate abstract concepts considered difficult for students to visualise and increase their interest and motivation in the learning process. Therefore, teachers need further training to implement AR optimally in science learning. Keywords: Augmented reality, science education, teachers' perspectives, technology, TPACK framework
Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs

Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.5M clinical notes describing 10,000 patients diagnosed with lung cancer at the Department of Veterans Affairs. The sentiment score of notes was calculated for two years after date of diagnosis and evaluated against a lab test (platelet count) and a combination of data points (treatments). We found that the oncology specific labMT dictionary, after re-calibration for the clinical oncology domain, produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.
The side-effects of 5G between hypothesis and theory

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Hassan Hassoon ALDelfi, Qaysar S. Mahdi, Behcet Celik; The side-effects of 5G between hypothesis and theory. AIP Conference Proceedings 25 January 2023; 2554 (1): 080001. https://doi.org/10.1063/5.0104345 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A pilot study for a non-invasive system for detection of malignancy in canine subcutaneous and cutaneous masses using machine learning

Early diagnosis of cancer enhances treatment planning and improves prognosis. Many masses presenting to veterinary clinics are difficult to diagnose without using invasive, time-consuming, and costly tests. Our objective was to perform a preliminary proof-of-concept for the HT Vista device, a novel artificial intelligence-based thermal imaging system, developed and designed to differentiate benign from malignant, cutaneous and subcutaneous masses in dogs.Forty-five dogs with a total of 69 masses were recruited. Each mass was clipped and heated by the HT Vista device. The heat emitted by the mass and its adjacent healthy tissue was automatically recorded using a built-in thermal camera. The thermal data from both areas were subsequently analyzed using an Artificial Intelligence algorithm. Cytology and/or biopsy results were later compared to the results obtained from the HT Vista system and used to train the algorithm. Validation was done using a "Leave One Out" cross-validation to determine the algorithm's performance.The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the system were 90%, 93%, 88%, 83%, and 95%, respectively for all masses.We propose that this novel system, with further development, could be used to provide a decision-support tool enabling clinicians to differentiate between benign lesions and those requiring additional diagnostics. Our study also provides a proof-of-concept for ongoing prospective trials for cancer diagnosis using advanced thermodynamics and machine learning procedures in companion dogs.