Reviewerly/exHarmony huggingface dataset:
Generative AI and composing: an intergenerational conversation among literacy scholars

Purpose The purpose of this paper is to provide a transcript of a dialogue among literacy educators and researchers on the impact of generative aritficial intelligence (AI) in the field. In the spring of 2023, a lively conversation emerged on the National Council of Research on Language and Literacy (NCRLL)’s listserv. Stephanie initiated the conversation by sharing an op-ed she wrote for Atlanta Journal-Constitution about the rise of ChatGPT and similar generative AI platforms, moving beyond the general public’s concerns about student cheating and robot takeovers. NCRLL then convened a webinar of eight leading scholars in writing and literacies development, inspired by that listerv conversation and an organizational interest in promoting intergenerational collaboration among literacy scholars. Design/methodology/approach As former doctoral students of two of the panel participants, webinar facilitators Grace and Victoria positioned themselves primarily as learners about this topic and gathered questions from colleagues, P-16 practitioners and those outside the field of education to assess the concerns and wonderings that ChatGPT and generative AI have raised. The following webinar conversation was recorded on two different days due to scheduling conflicts. It has been merged and edited into one dialogue for coherence and convergence. Findings Panel participants raise a host of questions and issues that go beyond topics of ethics, morality and basic writing instruction. Furthermore, in dialogue with one another, they describe possibilities for meaningful pedagogy and critical literacy to ensure that generative AI is used for a socially just future for students. While the discussion addressed matters of pedagogy, definitions of literacy and the purpose of (literacy) education, other themes included a critique of capitalism; an interrogation of the systems of power and oppression involved in using generative AI; and the philosophical, ontological, ethical and practical life questions about being human. Originality/value This paper provides a glimpse into one of the first panel conversations about ChatGPT and generative AI in the field of literacy. Not only are the panel members respected scholars in the field, they are also former doctoral students and advisors of one another, thus positioning all involved as both learners and teachers of this new technology.
Breast Cancer Detection Using Image Processing

Breast cancer remains a significant health concern for women worldwide, and early detection is critical for better treatment outcomes Since there are so many mammograms conducted each day, it is still very difficult to automatically detect and classify the masses, which is necessary for radiologists to properly identify masses in mammograms. In this study, we present a unique You Only Look Once (YOLO) version 7-based computer-aided diagnosis (CAD) system to assist radiologists in precisely detecting and localizing lesions in full and cropped pictures. YOLO is a popular object detection system, and the most recent version, YOLOv7, is capable of detecting objects in high-resolution images with a high frame rate. Mammography, a specialized imaging method used for breast cancer screening and diagnosis, might considerably benefit from automated CAD systems like the one suggested in this work. The creation of such systems has produced encouraging effects in terms of increasing the effectiveness and precision of breast cancer diagnosis, perhaps lightening the burden of radiologists and enhancing patient outcomes. A promising area with tremendous potential for development is the application of AI-based systems for medical imaging analysis.
Cognitive and embodied mapping of data: an examination of children’s spatial thinking in data physicalization

Understanding and effectively using visual representations is important to learning science, technology, engineering, and mathematics (STEM). Various techniques to visualize information, such as two- and three-dimensional graphs, diagrams, and models, not only expand our capacity to work with different types of information but also actively recruit our visual–spatial thinking. Data physicalization is emerging as a beginner-friendly approach to construct information visualization. Mapping intangible data onto tangible artifacts that possess visual, spatial, and physical properties demands an interplay of spatial thinking and hands-on manipulation. Much existing literature has explored using formatted infographics to aid learning and spatial thinking development. However, there is limited insight into how children may leverage their spatial thinking to create information visualizations, particularly tangible ones. This case study documented the data physicalization activities organized in two design classrooms of an international school in Netherlands, with 37 children aged 11–12. Seven themes relevant to spatial thinking were identified from multimodal evidence gathered from the data physicalization artifacts, classroom videos and recordings of children’s making process, and semi-structured interviews with children. Our findings suggested that these children generated various ideas to create visual–spatial forms for data with the materials at hand, such as mapping quantities to tangible materials of different sizes, using spatial ordinal arrangement, and unitizing materials to set visual parameters. Meanwhile, they evaluated and adjusted the visual–spatial properties of these materials according to the numerical data they had, crafting feasibility, and others’ spatial perspectives. What was particularly interesting in our findings was children’s iteration on their visual–spatial understandings of the intangible numerical values and the tangible materials throughout the embodied making processes. Overall, this study illustrated the different types of spatial thinking children applied to create their data physicalizations and offered insights into how embodied experiences accompanying the open-ended visualization challenge allowed children to explore and construct spatial understandings.
Exploring blockchain implementation challenges in the context of healthcare supply chain (HCSC)

Blockchain technology enables supply chains to share and distribute data in secured and decentralised environments through validators who trust and validate transactions.The use of blockchain was crucial in the context of the healthcare supply chain (HCSC) during COVID-19, where transactions were secure, and links throughout the supply chain were physically disrupted.Current research has focused on the blockchain application in industry, reviewing its architecture and the mechanisms involved.However, limited empirical studies consider HCSCs and the associated implementation challenges.In this sense, our case study of Indian hospitals has engaged with various stakeholders of the HCSC including clinicians, nurses, doctors, hospital managers, and digital healthcare equipment companies to explore the context to answer the following research questions: (i) What is the impact of the blockchain technology in the HCSC context? and (ii) What are the challenges faced in utilising blockchain technologies in the HCSC?Our findings indicate that the challenges blockchain implementation faced were based on variations in patients' treatment needs, data storage and privacy, interoperability, digital transformation, technology resistance, training and skills development, resource restraints and capabilities, and supply chain-wide collaboration.The study emphasises challenges for Blockchain in HCSC setting that both practitioners and academics need to be cognisant of.
Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices

The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss.
Hardness of the maximum-independent-set problem on unit-disk graphs and prospects for quantum speedups

Rydberg atom arrays are among the leading contenders for the demonstration of quantum speedups. Motivated by recent experiments with up to 289 qubits [Ebadi et al., Science 376, 1209 (2022)], we study the maximum-independent-set problem on unit-disk graphs with a broader range of classical solvers beyond the scope of the original paper. We carry out extensive numerical studies and assess problem hardness, using both exact and heuristic algorithms. We find that quasiplanar instances with Union-Jack-like connectivity can be solved to optimality for up to thousands of nodes within minutes, with both custom and generic commercial solvers on commodity hardware, without any instance-specific fine-tuning. We also perform a scaling analysis, showing that by relaxing the constraints on the classical simulated annealing algorithms considered in Ebadi et al., our implementation is competitive with the quantum algorithms. Conversely, instances with larger connectivity or less structure are shown to display a time-to-solution potentially orders of magnitudes larger. Based on these results, we propose protocols to systematically tune problem hardness, motivating experiments with Rydberg atom arrays on instances orders of magnitude harder (for established classical solvers) than previously studied.
Development of Android-based learning media using Smart Apps Creator on high school trigonometry material

This research is motivated by students' low enthusiasm to participate in mathematics. Students are more interested in using technological developments in learning because they look attractive, simple, and practical. This research aims to produce learning media based on Android using a valid and practical Smart Apps Creator. This research type is often known as Research and Development (R&amp;D) using the ADDIE development model (Analyze, Design, Development, Implementation, Evaluation). The participants in this study were XI IPA students at SMAN 1 X Koto in the Tanah Datar region. Validation questionnaires and practicality test questionnaires were the instruments utilized in this investigation. The percentage of the final assessment of the validity of learning media was 92.5%, with a very valid category based on the findings of the validator's assessment, which included media specialists and material experts. The teacher received a total score of 100% in the extremely practical area for the practicality rating. The pupils' ultimate practicality score fell into the very practical group with an 83.24% percentage. With a typical rate of 91.6%, using media is feasible. Based on the study's findings, the Smart Apps Creator-based Android learning resources for class XI IPA SMAN 1 X Koto, Tanah Datar Regency have been deemed legitimate and workable for usage by instructors and students.
Students’ perceptions of using ChatGPT in a physics class as a virtual tutor

Abstract The latest development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has drawn the attention of educational researchers and practitioners. We have witnessed many innovative uses of ChatGPT in STEM classrooms. However, studies regarding students’ perceptions of ChatGPT as a virtual tutoring tool in STEM education are rare. The current study investigated undergraduate students’ perceptions of using ChatGPT in a physics class as an assistant tool for addressing physics questions. Specifically, the study examined the accuracy of ChatGPT in answering physics questions, the relationship between students’ ChatGPT trust levels and answer accuracy, and the influence of trust on students’ perceptions of ChatGPT. Our finding indicates that despite the inaccuracy of GenAI in question answering, most students trust its ability to provide correct answers. Trust in GenAI is also associated with students’ perceptions of GenAI. In addition, this study sheds light on students’ misconceptions toward GenAI and provides suggestions for future considerations in AI literacy teaching and research.
A Study on the Benefits and Drawbacks of Adaptivity in AI-generated Explanations

It is commonly assumed that explanations should be tailored to the addressee in order to yield higher understanding. Consequently, much work on explainable intelligent agents has been directed to user-adapted explanations. However, recent studies show ambiguous results with regard to the efficiency of adaptive and non-adaptive explanations. This raises the question whether an explanation, generated by a socially interactive agent, should be adapted. In this paper, we present a general approach to adaptive explanation generation as a non-stationary decision process, and we study the benefits and pitfalls of adapting explanations in an ongoing interaction with a user. Specifically, we report results from a between-subject online evaluation in a game explanation domain with three conditions (non-interactive, interactive but non-adaptive, adaptive). Results show that the decision for or against adaptivity depends on the goal of the explanation, the complexity of the domain and external constraints. Based on the collected data we discuss challenges that arise from the individuality of adaptive dialogues, such as comparability and the tendency to produce results with a large variance.
Adversarial Attacking and Defensing Modulation Recognition With Deep Learning in Cognitive Radio-Enabled IoT

Modulation recognition using deep learning (DL) can efficiently recognize modulated signals in cognitive radio-enabled Internet of Things (IoT). However, it is vulnerable to the attack of adversarial examples designed by attackers, leading to a decrease in its accuracy. Different adversarial techniques can be used for attacks, but these attacks have limited efficiency. This paper proposes a double loop iterative method. Different from the traditional attack methods, the new method designs an additional external loop iteration for high efficiency. When generating adversarial examples, the initial conditions of each iteration can be updated as the number of iterations changes, so that the adversarial examples can cross the decision boundary of the model as much as possible. In addition, this paper uses knowledge distillation to improve the traditional adversarial training defense, which improves the robustness of the model. Simulation results show that the proposed attack and defense methods have better performance than traditional methods.
On the Impact of Label Noise in Federated Learning

Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is less explored. This paper aims to fill this gap by providing a quantitative study on the impact of label noise on FL. We derive an upper bound for the generalization error that is linear in the summation of clients' label noise levels. Then we conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms. Our empirical results show that the global model accuracy linearly decreases as the noise level increases, which is consistent with our theoretical analysis. We further find that label noise slows down the convergence of FL training, and the global model tends to overfit when the noise level is high.
Realization of photon correlations beyond the linear optics limit

Linear optical transformations of multiple single-photon inputs are fundamental for the development of photonic quantum technologies. Various nonclassical correlations can already be observed directly in states generated using only single-photon inputs and linear optics transformations. However, some quantum correlations require additional operations, and states that exhibit such correlations are classified as non-Fock states. Here, we demonstrate the generation of a two-photon three-mode non-Fock state that exhibits conditional quantum coherences that can only be achieved by non-Fock states. We determine the fidelity of the non-Fock state based on experimentally observed conditional visibilities that characterize the state and compare the result to the fidelity bounds for different classes of Fock and non-Fock states. Our experimental verification of the non-Fock character of the state provides insights into the technological requirements needed to achieve nonclassical correlations in multiphoton quantum optics.
Geo-Foundation Models: Reality, Gaps and Opportunities

With the recent rapid advances of revolutionary AI models such as ChatGPT, foundation models have become a main topic for the discussion of future AI. Despite the excitement, the success is still limited to specific types of tasks. Particularly, ChatGPT and similar foundation models have unique characteristics that are difficult to replicate for most geospatial tasks. This paper envisions several major challenges and opportunities in the creation of geospatial foundation (geo-foundation) models, as well as potential future adoption scenarios. We also expect that a major success story is necessary for geo-foundation models to take off in the long term.
A Scalable Inclusive Security Intervention to Center Marginalized &amp; Vulnerable Populations in Security &amp; Privacy Design

Research in computer security has increasingly considered the needs of marginalized and vulnerable groups in technology. Through this work, we hope to translate this research movement into practice and, ultimately, cause designers-in-training (and, eventually, designers) to consider a more inclusive range of stakeholders. Thus, we created an educational intervention to center marginalized and vulnerable populations in the context of threat modeling. We find that computer security students are more likely to consider unique threats and vulnerabilities facing marginalized and vulnerable populations after being exposed to an intervention prompting them to think about populations that might often be overlooked. We suggest practical methods to teach designers-in-training inclusive methods in computer security and discuss other possible adoptions of this practice across the field. This work is part of an important shift toward inclusive security that centers marginalized and vulnerable populations both in research and in practice.
Quantum algorithm for solving open-system dynamics on quantum computers using noise

In this paper we present a quantum algorithm that uses noise as a resource. The goal of our quantum algorithm is the calculation of operator averages of an open quantum system evolving in time. Selected low-noise system qubits and noisy bath qubits represent the system and the bath of the open quantum system. All incoherent qubit noise can be mapped to bath spectral functions. The form of the spectral functions can be tuned digitally, allowing for the time evolution of a wide range of open-system models at finite temperature. We study the feasibility of this approach with a focus on the solution of the spin-boson model and assume intrinsic qubit noise that is dominated by damping and dephasing. We find that classes of open quantum systems exist where our algorithm performs very well, even with gate errors as high as 1%. In general the presented algorithm performs best if the system-bath interactions can be decomposed into native gates.
Variational generation of spin squeezing on one-dimensional quantum devices with nearest-neighbor interactions

Efficient preparation of spin-squeezed states is important for quantum-enhanced metrology. Current protocols for generating strong spin squeezing rely on either high dimensionality or long-range interactions. A key challenge is how to generate considerable spin squeezing in one-dimensional systems with only nearest-neighbor interactions. Here, we develop variational spin-squeezing algorithms to solve this problem. We consider both digital and analog quantum circuits for these variational algorithms. After the closed optimization loop of the variational spin-squeezing algorithms, the generated squeezing can be comparable to the strongest squeezing created from two-axis twisting. By analyzing the experimental imperfections, the variational spin-squeezing algorithms proposed in this paper are feasible in recently developed noisy intermediate-scale quantum computers.
Reversible Data Hiding in Encrypted Images Based on Two-Round Image Interpolation

The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation in the decrypted domain. Of these mechanisms, pixel prediction is among the most frequently used, directly affecting the capacity and fidelity. In this paper, we propose a novel method that uses a two-round interpolation mechanism to enhance pixel prediction precision while preserving a large number of carrier pixels. In the proposed method, the content owner uses a stream cipher to encrypt the image as a carrier. The data hider flips specific LSBs of the encrypted image for data embedding. On the receiver side, the process of data extraction and image recovery is divided into two stages. In each stage, based on the varying distributions of the original or recovered pixels with the carrier pixels, the corresponding pixel interpolation schemes are used to accurately predict the pixels for data extraction and image recovery. The results demonstrate that the proposed method can efficiently improve the capacity and fidelity with full reversibility compared to existing VRAE RDHEI methods.