Reviewerly/exHarmony huggingface dataset:
Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer’s and Parkinson’s diseases

Abstract Objective Biases within probabilistic electronic phenotyping algorithms are largely unexplored. In this work, we characterize differences in subgroup performance of phenotyping algorithms for Alzheimer’s disease and related dementias (ADRD) in older adults. Materials and methods We created an experimental framework to characterize the performance of probabilistic phenotyping algorithms under different racial distributions allowing us to identify which algorithms may have differential performance, by how much, and under what conditions. We relied on rule-based phenotype definitions as reference to evaluate probabilistic phenotype algorithms created using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation framework. Results We demonstrate that some algorithms have performance variations anywhere from 3% to 30% for different populations, even when not using race as an input variable. We show that while performance differences in subgroups are not present for all phenotypes, they do affect some phenotypes and groups more disproportionately than others. Discussion Our analysis establishes the need for a robust evaluation framework for subgroup differences. The underlying patient populations for the algorithms showing subgroup performance differences have great variance between model features when compared with the phenotypes with little to no differences. Conclusion We have created a framework to identify systematic differences in the performance of probabilistic phenotyping algorithms specifically in the context of ADRD as a use case. Differences in subgroup performance of probabilistic phenotyping algorithms are not widespread nor do they occur consistently. This highlights the great need for careful ongoing monitoring to evaluate, measure, and try to mitigate such differences.
A-MAZE Artists Update 2023: Boundless – Worlds in Flux: Overcoming boundaries to virtual world integration in traditional heritage settings

The A-Maze artists Collective has been developing a Virtual World Hub for artists and their audienceparticipants, reaching out to new audiences and to enable deep engagement.Artworks are created as experiences using innovative technology offering immersive and deep engagement.This paper describes how, building on previous development, A-Maze Artists Collective continue to develop 'Boundless -Worlds in Fluxa Virtual Reality platform with a hub leading to different Artists' spaces.During 2022, A-Maze has reached out to new audiences and deepened engagement through collaboration with museums and new user groups.There has been churn in the team and this year welcomes new artists, developers, art professionals and partnerships.This paper indicates recent challenges, methods, partners and insights on this development.The project has entered into a deeper level around overcoming boundaries including virtual world integration and implementation of the virtual world within traditional heritage settings.A schematic is indicated which feeds into development design and technical choices.Future development includes an analysis of web-hosted versus devicehosted downloadable applications offering digital interactivity and immersive experience to its users.
Strengthening Structural Baselines for Graph Classification Using Local Topological Profile

We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. Our study focuses on model evaluation in the context of the recently developed fair evaluation framework, which defines rigorous routines for model selection and evaluation for graph classification, ensuring reproducibility and comparability of the results. Based on the obtained insights, we propose a new baseline algorithm called Local Topological Profile (LTP), which extends LDP by using additional centrality measures and local vertex descriptors. The new approach provides the results outperforming or very close to the latest GNNs for all datasets used. Specifically, state-of-the-art results were obtained for 4 out of 9 benchmark datasets. We also consider computational aspects of LDP-based feature extraction and model construction to propose practical improvements affecting execution speed and scalability. This allows for handling modern, large datasets and extends the portfolio of benchmarks used in graph representation learning. As the outcome of our work, we obtained LTP as a simple to understand, fast and scalable, still robust baseline, capable of outcompeting modern graph classification models such as Graph Isomorphism Network (GIN). We provide open-source implementation at GitHub .
AI Cognitive - Based Systems Supporting Learning Processes

One of the most important issues in modern educational activities is equal opportunities in learning for different students and persons, having various social, technical, and economic conditions or limitations. To achieve such goal, we propose to use different classes of cognitive information systems, which perform cognitive resonance processes, and allow to support visual data understanding and semantic content evaluation. Such systems can facilitate learning activities at different levels, and have a positive influence on education processes, by improving perception abilities for students. In particular cognitive information systems will be proposed as a tool for creation of user-oriented learning protocols. Such protocols consider users’ perception capabilities, and can be dependent on external features, in which person try to acquire some new knowledge, abilities and competence.
How Application Ease The Assessment Process in EFL Classroom

The learning process carried out in Indonesia is still dominated by large classes. It contains more than 20 students. This situation sometimes makes it difficult for teachers to conduct assessments because assessing and evaluating each student takes much time. Nowadays, the assessment process is quite diverse; teachers do not only do paper-based tests but can use application assistance. This study aims to see how well the Pintar Apps help teachers conduct student assessments. The method used in this study is qualitative, with instruments in the form of class observations and interviews. The subjects were 15 and 3 English teachers students of SMP Negeri 2 Tayu. The study results revealed that the application helps the student assessment process faster and more accurately. The teacher does not need to check the results of student answers one by one because the system automatically checks them. Pintar Apps as the student assessment application are beneficial for teachers in managing assessment activities in class. Furthermore, Pintar Apps even helps teachers administer the students who have used this application, and there is a list of them for all activities when using this application.
Implicit contact-rich manipulation planning for a manipulator with insufficient payload

Purpose This paper aims to study using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot’s maximum payload. Design/methodology/approach This paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method’s help, the authors plan contact-rich manipulation without particularly analyzing an object’s contact modes and their transitions. The planner and optimizer determine them automatically. Findings The authors conducted experiments and analyses using simulations and real-world executions to examine the method’s performance. The method successfully found manipulation motion that met contact, force and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. Originality/value This paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.
Docencia digital. Competencias específicas en profesores universitarios

The objective of the study was to compare the specific competencies for digital teaching in different university professors according to academic variables of interest. It had a quantitative approach and a non-experimental design with comparative scope. A total of 114 professors from an Ecuadorian and a Spanish university participated. The data were obtained through the Online Tutor Functions and Competencies Scale. Most of the professors showed all the specific competencies for virtual teaching with frequencies ranging from 81.6% to 100%. The results of the Kruskal-Wallis H-test showed no statistically significant differences in the comparisons made. Among the participants there was a predominance of those with less than six years of academic experience, those who worked full-time in teaching, those who preferred the face-to-face modality and those who stated that they did not perceive that they had problems with virtual teaching. No significant differences were identified between the groups studied and the specific competencies for the development of teaching.
Pedagogical Application of Visualization and Eye Tracking For Electrical Grid Management

Visualizations have been proven to be a beneficial pedagogical tool used in teaching complex concepts to students. Its application as an effective active learning method has been implemented in multiple sectors, such as arts, science, engineering, and social science. In this paper, we present a pilot study exploring the use of visualizations to facilitate students’ comprehension of electrical grid management principles in an active learning process. The methods for conducting the eye-tracking study are presented here, with the results available at the meeting. These results will illustrate how students engage with visualizations as part of the learning process. This study is part of a series of studies focused on examining how methods from human factors and ergonomics can be leveraged for evaluating electrical grid management control rooms. For instance, in a previous pilot study, eye tracking was used to evaluate situation awareness in electrical grid control rooms using visualizations. A comparison of the eye tracking results to the Cognitive Task Analysis (CTA), revealed four SA demons: misplaced salience, data overload, errant mental models, and attentional narrowing.
Design of interactive service for stray cat adoption based on shared platform

In order to solve the stray cat adoption problem, increase the sustainability of stray cat rescue activities and improve the current situation of the existing stray cat rescue platform, this study develops the service design practice through four stages: exploration, definition, development and evaluation. Firstly, the design process and the method of service design are analyzed through literature research. Secondly, the causes of stray cat problem and the current status of existing rescue platforms were investigated. In the exploration and definition stage, using user journey map, personas, and service blueprints to gain user’s needs. The study identified pain points and translated them into functions in this service design. In the evaluation stage, SUS test was used to verify the feasibility of the service design. This study constructed a new online cat raising service platform, including online cat raising service framework, APP design and innovative interaction ways. Continuously optimize the service experience of the existing stray cat support platform to attract users to continuously participate in the stray cat rescue activities. To provide theoretical support and reference for future rescue platforms for stray cats.
Metaverse on Education: A case study about users’ acceptance of technology

This paper aims to present a study about people’s acceptance of using the Metaverse technology. The education context is the focus of intervention justified by the massification of distant learning powered by the new challenges faced by this sector during the lookdowns caused by the COVID-19 pandemic. Also, with technological development, a new paradigm regarding interactions may be able to change the educational dynamics. Thus, this pilot study aims to investigate the acceptance level of potential users of this technology to identify factors that can influence technology use and enrich the debate about new interactive worlds. For this, the Unified Theory Acceptance and Use of Technology (UTAUT) questionary was adapted and made available through an online platform. The total sample had 148 valid questionaries. Overall results show a low level of anxiety, with most people reporting that they didn’t feel scared about using metaverse for online education. They also report interest, mainly related to the capacity to help with some tasks and optimize the educational tools. Researching users’ acceptance before investing in effective development could promote a more reliable result, enhancing cost x benefit issues. Users’ acceptance is even more critical when social and cultural aspects are involved, such as changes in the educational paradigm. Researching how ready our society is to accept a new concept - such as having classes or doing educational expeditions within the metaverse through VR - and its impacts provide a glance at how the future can be designed sustainably, helping to shape the way technology takes part of our daily life.
Developing comprehensive sex education smart tools for the young generation with co-design: sex education for parents and children at home

This article introduces the use of design as a communication scenario in collaborative design to provide a holistic, intelligent education tool in the home setting. One out of every ten children has been subjected to sexual harm of varying extents. Implementing children's family sex education can endow children with the required correct knowledge and attitudes regarding sex. Co-design can help the children's family design forms, arrangement of sex, and other sensitive issues relaxed mind. The design prototype to develop a joint design workshop between children and parents was created during the co-design process. The interactive professional books for lessons from discussing body perception and alarms to discussing sex education knowledge comprehensively were expanded. It was concluded that the co-design method is effective in sex education intervention, and smart tools can link sex education institutions with families and empower children for equal dialogue.
Which quantum computer is the most powerful?

IonQ has become the latest firm to claim its forthcoming device is better than any other. But how do you benchmark a quantum computer, asks Matthew Sparkes
Redesigning the Colega Application Interface and Interaction Using the Learner-Centered Design Method

In the rapid development of technology, almost all activities are carried out online, activities become timeless so that many students have difficulty in managing time between lectures and other activities. So that students must be able to organize their activity schedule, record tasks and activities that must be completed. One of the efforts that can be made is to use colega products which are technology products to remind activities. The purpose of this research is to redesign the Colega mobile application interface that is suitable and easy to use based on student needs to increase the level of usability with the learner-centered design method., this method is a design process that focuses on the needs of learner, by performing 5 stages, namely Specify the context of use, Specify user requirements, Product design solution, and Evaluate against user requirements. The product developed using this method, the principle of this method is a collaboration that allows learner to develop, test, and analyze their ideas for the product to be made. Interface design and interaction testing using the System Usability Scale. the results of usability testing on the interface obtained a value of 60.62 to 78.12. With the test results obtained an increase in value in the usability aspect. So it can be concluded that the usability value falls into the good and acceptable category.
Improved Continually Evolved Classifiers for Few-Shot Class-Incremental Learning

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Few-shot class-incremental learning</i> (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The scarcity of new training data will seriously destroy the model’s stability and plasticity. Continually Evolved Classifiers (CEC)[1], a kind of framework, maintains the stability by freezing the encoder and achieves the plasticity by evolving the classifier along with a pseudo incremental learning scheme. However, the performance of CEC is limited due to 1) inequitable information gains between classifier weights and test features, and 2) inefficient learning task construction strategy. To address the first issue, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knowledge-guided Relation Refinement Module</i> (KRRM) to update both the classifier weights and test features. The main function of KRRM is achieved through cross-attention to propagate the knowledge represented by old encoded data. To address the second issue, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pseudo Incremental relation Refinement Learning</i> (PIRL) that utilizes a novel hard concepts mining strategy to mine hard concept tasks globally and locally. By successfully addressing the two issues, our proposed method, named Improved Continually Evolved Classifiers (CEC+), extends the potential of CEC without introducing any additional parameters. More precisely, extensive experiments on CIFAR100, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> ImageNet, and Caltech-UCSD Birds-200-2011, demonstrate that our proposed method surpasses prior state-of-the-art methods.
Research on Digital Application of Cultural Heritage Under the Background of AI

Intangible cultural heritage has very precious historical value, it represents the existence of a period of history, and it is worthy of our protection and study.Therefore, we should pay attention to the protection and utilization of intangible cultural heritage.This article will study the protection and utilization of intangible cultural heritage under the background of digitalization.Compared with the tangible cultural heritage with a certain form, the protection of intangible cultural heritage is more difficult.In addition, intangible cultural heritage is a non-renewable resource, which adds difficulty to the protection of intangible cultural heritage.In the context of digitalization, it provides a way for the protection and use of intangible cultural heritage.This article is to explore how to protect and utilize intangible cultural heritage under the background of digitalization.
Region-based Convolutional Neural Network (R-CNN) architecture for auto-cropping of pancreatic computed tomography

Automatic pancreas detection and cropping with high precision from medical images is an important yet challenging problem for medical image analysis and Computer-Aided Diagnosis (CAD). Factors relating to the limited availability of image data and segmentation methodology hinder this task. High variability in the location of the pancreas,which occupies a very small area of the pancreatic Computed Tomography (CT) scans,and the anatomy of organs also add to the list of issues. These challenges necessitate an urgent need for the development of localization and auto-cropping methods of the region of interest (ROI). This paper presents the results obtained by the implementation of Region-based Convolutional Neural Network (RCNN)-Crop inspired by the Region Proposal Network (RPN) and Feature Pyramid Network (FPN) to localize the pancreas by building bounding boxes and auto-crop the ROI obtained from various other organs in the pancreatic CT scans and has a Mean Average Precision (mAP) of 28.10% for the dataset provided.
Curiosity Creates Diversity in Policy Search

When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another. A way to overcome this is to use intrinsic motivation in order to explore new transitions until a reward is found. In this work, we use a recently proposed definition of intrinsic motivation, Curiosity, in an evolutionary policy search method. We propose Curiosity-ES, 1 an evolutionary strategy adapted to use Curiosity as a fitness metric. We compare Curiosity-ES with other evolutionary algorithms intended for exploration, as well as with Curiosity-based reinforcement learning, and find that Curiosity-ES can generate higher diversity without the need for an explicit diversity criterion and leads to more policies which find reward.
The Development of Competencies for Use in Online Learning During the Covid-19 Pandemic: Insights from the hospitality management degree program at the Universidad Panamericana, Mexico City campus

This study aims to comparatively analyze students and professors' perceptions regarding the development of competencies through the online format of a bachelor's degree program in hospitality management during three semesters of stay-at-home learning. The goal is to understand whether the tools and teaching resources used by faculty adequately met the objectives of the bachelor program, which has as one of its main objectives the development of competencies. Using the two variables from the TAM Model (i.e., perceived ease of use and perceived usefulness) and the types of technological resources that students and professors used, we found both groups saw the accessibility and ease of use of technological tools as simple and believe that they are useful or very useful for learning. Likewise, they agree that teamwork and change management were the competences they most developed during the pandemic, which play an important role in digital literacy.
What Will Happen When We Radically Simplify t-SNE and UMAP Visualization Algorithms? Is It Worth Doing So?

We investigate how the quality and computational complexity of the golden standards of high-dimensional data (HDD) visualisation - the t-SNE and UMAP algorithms - change with their successive simplifications. We show that by radically reducing the number of the utilised nearest neighbours, introducing binary distances between the samples, and simplifying the loss function, the resulting IVHD algorithm still reconstructs with sufficient precision both local and, particularly, global properties of HDD topology. Although inferior to its competitors for the most moderate data sizes ( $$M$$ $$10^5$$ samples), IVHD appears many times faster than state-of-the-art algorithms and reveals its power for multi-million-element datasets for which baseline methods fail in a reasonable computational time.