Reviewerly/exHarmony huggingface dataset:
A2M-LEUK: attention-augmented algorithm for blood cancer detection in children

Abstract Leukemia is a malignancy that affects the blood and bone marrow. Its detection and classification are conventionally done through labor-intensive and specialized methods. The diagnosis of blood cancer in children is a critical task that requires high precision and accuracy. This study proposes a novel approach utilizing attention mechanism-based machine learning in conjunction with image processing techniques for the precise detection and classification of leukemia cells. The proposed attention-augmented algorithm for blood cancer detection in children (A2M-LEUK) is an innovative algorithm that leverages attention mechanisms to improve the detection of blood cancer in children. A2M-LEUK was evaluated on a dataset of blood cell images and achieved remarkable performance metrics: Precision = 99.97%, Recall = 100.00%, F1-score = 99.98%, and Accuracy = 99.98%. These results indicate the high accuracy and sensitivity of the proposed approach in identifying and categorizing leukemia, and its potential to reduce the workload of medical professionals and improve the diagnosis of leukemia. The proposed method provides a promising approach for accurate and efficient detection and classification of leukemia cells, which could potentially improve the diagnosis and treatment of leukemia. Overall, A2M-LEUK improves the diagnosis of leukemia in children and reduces the workload of medical professionals.
Future quantum workforce: Competences, requirements, and forecasts

With the increasing industrial relevance of new quantum technologies, a well-educated quantum workforce becomes increasingly crucial. The foreseeable lack of workforce raises important questions. What are the expectations regarding the future relevance of second-generation quantum technologies? What are the requirements for the workforce in the coming quantum industry? Which competences, knowledge, and skills should future employees have? In this paper, we report the results of our study that were aimed at mapping requirements and forecasts for the future quantum workforce. Our study consisted of three consecutive survey rounds. In total, we gathered 188 responses from industry and academic experts across Europe. Our study results served as an input for the development of the European Competence Framework for Quantum Technologies, delivered by the project QTEdu CSA for the European Quantum Flagship. In addition, we will discuss predictions from experts related to the future quantum workforce, including the expected industrial relevance of the main areas of quantum technologies, the need for educational efforts, and the expected influence of quantum technologies on everyday life.3 MoreReceived 18 August 2022Accepted 10 April 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.010137Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEducational policyInstructional materials developmentQuantum engineeringTechnologyProfessional TopicsFacultyPhysics Education Research
Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.
This Sample Seems to Be Good Enough! Assessing Coverage and Temporal Reliability of Twitter’s Academic API

Because of its willingness to share data with academia and industry, Twitter has been the primary social media platform for scientific research as well as for consulting businesses and governments in the last decade. In recent years, a series of publications have studied and criticized Twitter's APIs and Twitter has partially adapted its existing data streams. The newest Twitter API for Academic Research allows to "access Twitter's real-time and historical public data with additional features and functionality that support collecting more precise, complete, and unbiased datasets. The main new feature of this API is the possibility of accessing the full archive of all historic Tweets. In this article, we will take a closer look at the Academic API and will try to answer two questions. First, are the datasets collected with the Academic API complete? Secondly, since Twitter's Academic API delivers historic Tweets as represented on Twitter at the time of data collection, we need to understand how much data is lost over time due to Tweet and account removal from the platform. Our work shows evidence that Twitter's Academic API can indeed create (almost) complete samples of Twitter data based on a wide variety of search terms. We also provide evidence that Twitter's data endpoint v2 delivers better samples than the previously used endpoint v1.1. Furthermore, collecting Tweets with the Academic API at the time of studying a phenomenon rather than creating local archives of stored Tweets, allows for a straightforward way of following Twitter's developer agreement. Finally, we will also discuss technical artifacts and implications of the Academic API. We hope that our work can add another layer of understanding of Twitter data collections leading to more reliable studies of human behavior via social media data.
How do physics students evaluate artificial intelligence responses on comprehension questions? A study on the perceived scientific accuracy and linguistic quality of ChatGPT

This study aimed at evaluating how students perceive the linguistic quality and scientific accuracy of ChatGPT responses to physics comprehension questions. A total of 102 first- and second-year physics students were confronted with three questions of progressing difficulty from introductory mechanics (rolling motion, waves, and fluid dynamics). Each question was presented with four different responses. All responses were attributed to ChatGPT, but in reality one sample solution was created by the researchers. All ChatGPT responses obtained in this study were wrong, imprecise, incomplete, or misleading. We found little differences in the perceived linguistic quality between ChatGPT responses and the sample solution. However, the students rated the overall scientific accuracy of the responses significantly differently, with the sample solution being rated best for the questions of low and medium difficulty. The discrepancy between the sample solution and the ChatGPT responses increased with the level of self-assessed knowledge of the question content. For the question of highest difficulty (fluid dynamics) that was unknown to most students, a ChatGPT response was rated just as good as the sample solution. Thus, this study provides data on the students' perception of ChatGPT responses and the factors influencing their perception. The results highlight the need for careful evaluation of ChatGPT responses both by instructors and students, particularly regarding scientific accuracy. Therefore, future research could explore the potential of similar "spot the bot"-activities in physics education to foster students' critical thinking skills.
Impact of Metaverse Technology on Student Engagement and Academic Performance: The Mediating Role of Learning Motivation

Metaverse technology, encompassing virtual reality (VR) and augmented reality (AR), offers immersive and interactive learning environments that can enhance student engagement. The personalized and adaptive nature of metaverse experiences has the potential to spark students' intrinsic motivation and promote active involvement in their learning. In order to evaluate the impact of metaverse technology on academic performance and level of engagement this research was aimed to assess the academic performance with mediating effect of learning motivation. We conducted an empirical research through an online survey that initially utilized to measure the collected data. Data collected from 33 educational institution located in Dubai UAE. A total of 251 respondent’s data were utilized and assessed through structured equation modelling. SmartPLS 4 was used to check the model convergent validity, discriminant validity and hypothesis testing. The findings revealed student involvement can be positively impacted by the employment of metaverse technology, which in turn improves academic performance. However, it's critical to take into account potential access restrictions and the necessity for a well-rounded educational strategy that combines metaverse experiences with other teaching strategies. Future studies should focus on the precise mechanisms by which metaverse technology influences learning motivation, engagement, and performance, as this will give researchers important information for developing efficient metaverse-based educational interventions.
The NOOCs as a training strategy for teachers in the use of technological tools in primary education

Since their inception, Nano Massive Open Courses (NOOC) have been considered an alternative for the continuous training of teaching skills. Within the framework of an IB certification process, the primary education section of a private school in the city of Querétaro identified the need to train its teachers in the use of the Toddle App tool. The study was carried out from the methodological framework of action research, so three courses in NOOC format were designed and launched as a training strategy. The first stage of the investigation consisted of data collection through a questionnaire via Google Forms to obtain relevant information about the teachers' perception of the technological tool and in relation to the thematic areas of the training strategy. In the second stage, three NOOCs were designed and implemented. The final stage consisted of applying a questionnaire to obtain feedback data on the training strategy. The most relevant results suggest that most teachers consider the training modality to be adequate and sufficient. Furthermore, teachers consider the guide of an expert necessary to use the tool and affirm to have conceptual deficiencies about the different sections of the platform.
Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.
Resonance Fluorescence of a Chiral Artificial Atom

We demonstrate a superconducting artificial atom with strong unidirectional coupling to a microwave photonic waveguide. Our artificial atom is realized by coupling a transmon qubit to the waveguide at two spatially separated points with time-modulated interactions. Direction-sensitive interference arising from the parametric couplings in our scheme results in a nonreciprocal response, where we measure a forward/backward ratio of spontaneous emission exceeding 100. We verify the quantum nonlinear behavior of this artificial chiral atom by measuring the resonance fluorescence spectrum under a strong resonant drive and observing well-resolved Mollow triplets. Further, we demonstrate chirality for the second transition energy of the artificial atom and control it with a pulse sequence to realize a qubit-state-dependent nonreciprocal phase on itinerant photons. Our demonstration puts forth a superconducting hardware platform for the scalable realization of several key functionalities pursued within the paradigm of chiral quantum optics, including quantum networks with all-to-all connectivity, driven-dissipative stabilization of many-body entanglement, and the generation of complex nonclassical states of light.8 MoreReceived 21 December 2022Revised 12 April 2023Accepted 26 April 2023DOI:https://doi.org/10.1103/PhysRevX.13.021039Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systems & decoherenceQuantum networksQuantum simulationQuantum Information
Accounting For Digital Assets

Abstract Recent years have seen developments in distributed ledger technologies, such as blockchain, that have led to significant growth in the number and type of digital assets available. In this article we review the current practice of the small number of firms globally reporting digital assets on their financial statements. We then assess potential treatments under current GAAP, namely as intangibles, inventory or financial instruments. Finally, we provide policy advice to standard setters, with a call to either develop a new stand‐alone standard or to amend the definition of financial instruments to include cryptocurrencies, to allow greater comparability and understandability in firms’ reporting. Recent years have seen significant growth in the number and type of digital assets available. We review the current practices of firms reporting digital assets. We then assess potential treatments under current GAAP. Finally, we provide policy advice to standard setters, with a call to either develop a new stand‐alone standard or to amend the definition of financial instruments.
Increasing the competence of future economists to improve the effectiveness of training in the context of achieving the principles of sustainable development

Objective: The study aims to shed light on the opportunity of using the KmPlot software in teaching the topics of further mathematics associated with the study of functions, as well as to substantiate the feasibility of its use. Methods: The conducted experimental study using the methods of observation, analysis, systematization, and pedagogical experiment provides qualitative and quantitative information on the expediency of using KmPlot in teaching further mathematics. Results: The results obtained indicate an increase in the level of learning outcomes when using the KmPlot software, i.e. improved learning outcomes of future economists in the study of further mathematics by means of freely distributed computer mathematical programs. Conclusion: The use of licensed software by Russian universities may become a topical problem in current realities. Free software products for learning further mathematics, such as graphing software, can serve as a good alternative.
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without differentiation, which ignores the difference among samples. In this paper, we propose an unsupervised domain adaptation model with sample weight learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network. We introduce the meta-optimization based update rule to learn this network end-to-end, which is guided by meta-classification loss on the selected pseudo-labeled target samples. Therefore, this network can fit a weighting function according to the cross-user WHAR task at hand, which is superior to existing sample differentiation rules fixed for special scenarios. Extensive experiments on three public WHAR datasets demonstrate that SWL-Adapt achieves the state-of-the-art performance on the cross-user WHAR task, outperforming the best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score, respectively.
MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning

Contrastive learning (CL), which can extract the information shared between different contrastive views, has become a popular paradigm for vision representation learning. Inspired by the success in computer vision, recent work introduces CL into graph modeling, dubbed as graph contrastive learning (GCL). However, generating contrastive views in graphs is more challenging than that in images, since we have little prior knowledge on how to significantly augment a graph without changing its labels. We argue that typical data augmentation techniques (e.g., edge dropping) in GCL cannot generate diverse enough contrastive views to filter out noises. Moreover, previous GCL methods employ two view encoders with exactly the same neural architecture and tied parameters, which further harms the diversity of augmented views. To address this limitation, we propose a novel paradigm named model augmented GCL (MA-GCL), which will focus on manipulating the architectures of view encoders instead of perturbing graph inputs. Specifically, we present three easy-to-implement model augmentation tricks for GCL, namely asymmetric, random and shuffling, which can respectively help alleviate high-frequency noises, enrich training instances and bring safer augmentations. All three tricks are compatible with typical data augmentations. Experimental results show that MA-GCL can achieve state-of-the-art performance on node classification benchmarks by applying the three tricks on a simple base model. Extensive studies also validate our motivation and the effectiveness of each trick. (Code, data and appendix are available at https://github.com/GXM1141/MA-GCL. )
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, but it has been widely shown that applying a vanilla MLP network alone is ineffective in learning complex feature interactions. As such, many two-stream models (e.g., Wide&amp;Deep, DeepFM, and DCN) have recently been proposed, aiming to integrate two parallel sub-networks to learn feature interactions from two different views for enhanced CTR prediction. In addition to one MLP stream that learns feature interactions implicitly, most of the existing research focuses on designing another stream to complement the MLP stream with explicitly enhanced feature interactions. Instead, this paper presents a simple two-stream feature interaction model, namely FinalMLP, which employs only MLPs in both streams yet achieves surprisingly strong performance. In contrast to sophisticated network design in each stream, our work enhances CTR modeling through a feature selection module, which produces differentiated feature inputs to two streams, and a group-wise bilinear fusion module, which effectively captures stream-level interactions across two streams. We show that FinalMLP achieves competitive or even better performance against many existing two-stream CTR models on four open benchmark datasets and also brings significant CTR improvements during an online A/B test in our industrial news recommender system. We envision that the simple yet effective FinalMLP model could serve as a new strong baseline for future development of two-stream CTR models. Our source code will be available at MindSpore/models and FuxiCTR/model_zoo.