Reviewerly/exHarmony huggingface dataset:
Finding the Top-K Heavy Hitters in Data Streams: A Reconfigurable Accelerator Based on an FPGA-Optimized Algorithm

This paper presents a novel approach for accelerating the top-k heavy hitters query in data streams using Field Programmable Gate Arrays (FPGAs). Current hardware acceleration approaches rely on the direct and strict mapping of software algorithms into hardware, limiting their performance and practicality due to the lack of hardware optimizations at an algorithmic level. The presented approach optimizes a well-known software algorithm by carefully relaxing some of its requirements to allow for the design of a practical and scalable hardware accelerator that outperforms current state-of-the-art accelerators while maintaining near-perfect accuracy. This paper details the design and implementation of an optimized FPGA accelerator specifically tailored for computing the top-k heavy hitters query in data streams. The presented accelerator is entirely specified at the C language level and is easily reproducible with High-Level Synthesis (HLS) tools. Implementation on Intel Arria 10 and Stratix 10 FPGAs using Intel HLS compiler showed promising results—outperforming prior state-of-the-art accelerators in terms of throughput and features.
Outlier Detection with Reinforcement Learning for Costly to Verify Data

Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues leading to the data error can change over time. An outlier detection approach should therefore be able to optimally use the knowledge gained from the verification of the ground truth and adjust accordingly. With advances in machine learning, this can be achieved by applying reinforcement learning on a statistical outlier detection approach. The approach uses an ensemble of proven outlier detection methods in combination with a reinforcement learning approach to tune the coefficients of the ensemble with every additional bit of data. The performance and the applicability of the reinforcement learning outlier detection approach are illustrated using granular data reported by Dutch insurers and pension funds under the Solvency II and FTK frameworks. The application shows that outliers can be identified by the ensemble learner. Moreover, applying the reinforcement learner on top of the ensemble model can further improve the results by optimising the coefficients of the ensemble learner.
Digital Divide, Local and Global? Surveying Augmented Reality Educational Usage in Europe and South America

Augmented Reality (AR) has the potential to enhance students’ learning experiences. To deploy AR as educational tool, access to the requisite infrastructure and readiness of teachers are basic conditions to be met. The lack thereof is the digital divide that can undermine the opportunity and ability to use AR educational applications (AREAs) for students and teachers. Such issues were identified in a survey with a sample of European teachers on their educational usage of AR. We aimed to study the digital divide more globally by running the same survey with teachers in South America. We collected 123 valid responses from ten South American countries. Comparing the results of the two datasets showed that the teachers sampled from both continents were facing the challenges of the digital divide to utilise AREAs. We discussed how these challenges could be addressed to allow both, teachers and students, to benefit from AR as educational tool.
Pocket English Master – Language Learning with Reinforcement Learning, Augmented Reality and Artificial Intelligence

English is the most spoken language in the world. Despite this, only 1.35 billion people worldwide use English as their first or second language of communication. English is the world’s main language, 83% of the world’s population do not speak English. As a result, most people must spend considerable money and time to learn English. Continuous practice ensures that the language develops as often as possible. Developed, Pocket English Master (PEM) mobile app assumes the role of a full-fledged English teacher to solve the above problems. This application entails conducting an early assessment of the student’s present English competence and directing him or her to the appropriate class. Using artificial intelligence, a curriculum is created that matches the knowledge level of the students. The application enables students to complete learning activities at their convenience by evaluating the student’s learning style using artificial intelligence and making the student’s daily activities efficient and aesthetically pleasing using reinforcement learning and algorithms. This includes an artificial intelligence component to improve students’ ability to speak and read English as well as their ability to engage in natural relationships. Also, the app incorporates augmented reality, real-time handwritten text scanning, and real-time grammar and spelling error correction.
Mobile English Learning: A Meta-analysis

The advantages of mobile learning (m-learning) in English education have been widely described in previous research; however, there is little evidence of its effects on student outcomes. To fill this research gap, we conducted a meta-analysis of 54 empirical studies to measure its impact on student achievement. In addition, we estimated the moderating effects of the pedagogical approach, the learning environment, the education level, the control treatment, and the mobile device. The results indicate that m-learning has a large impact $$(g=0.94)$$ on students’ achievement. This effect is influenced by the pedagogical approach, the education level, and the control treatment, but not by the learning environment or the mobile device. Finally, we explain the nature of these results in light of learning theories.
A Deep Learning System to Help Students Build Concept Maps

Nowadays, overwhelmed as people are by the amount of information available, it becomes more and more difficult to build an adequate cognitive process of knowledge building and discovery, on any one knowledge domain. Therefore, having knowledge-building tools at disposition, especially in the age of schooling, is of great importance. Knowledge building occurs by linking new concepts to already learned ones, thus connecting concepts together by means of semantic links representing their relationship. To accomplish this task, most of learners use Concept Maps, that is graphic tools, particularly suitable, to organize, represent and share knowledge. In fact, a Concept Map can explicitly express the knowledge of a person or group, about a given domain of interest: from primary school to university, and to professional/vocational training, Concept Maps can stimulate and unveil the occurrence of the so-called meaningful learning, according to the Ausubel’s learning theory. In this paper we investigate the use of a deep learning-based architecture, called TransH, designed for Knowledge Graph Embedding, to support the process of Concept Maps building. This approach has not been yet investigated for this particular educational task. Some preliminary case studies are discussed, confirming the potential of this approach.
Correction to ‘Optocoder: computational decoding of spatially indexed bead arrays’

[This corrects the article DOI: 10.1093/nargab/lqac042.].
Teachers’ digital pedagogies and experiences in virtual classrooms

The global pandemic has revealed a number of inequalities in education for students in Michigan, USA. Teachers were tasked with a sudden shift to online instruction as school closures across the state (and globe) forced education to operate in virtual classrooms. Given the continued need for online learning, it is necessary to examine the perspectives and experiences of teachers who worked throughout the school closures of spring 2020 and to identify ways to better support both teachers and students as they navigate virtual learning. The purpose of this exploratory qualitative study was to investigate K-12 teachers’ experiences shifting their classrooms online and their overall perspectives on the use of digital literacies as a central part of student learning. Reported in this paper are the survey results from 389 teachers’ perceptions of their effectiveness during the Covid-19 pandemic during the semester spring of 2020. Additionally, a randomly selected a subset of 12 elementary and 13 secondary teachers’ semi-structured interviews were analyzed. Unsurprisingly, teachers reported many challenges meeting their students’ needs as they transitioned to online learning. Findings revealed students who lacked access to reliable Internet and digital devices. Even when access was not an issue, students lacked the necessary digital literacy skills to navigate their online education. Teachers reported many challenges associated with online learning, particularly those related to student engagement, communication, and shifting pedagogies online. Therefore, there is a need for schools and curricula to include operational digital literacy skills earlier in formal education settings, and to prioritize these skills even when face-to-face instruction resumes.
Image Processing on Resource-Constrained Devices

The chapter portrays a new development in the field of embedded systems. It showcases the combination of Machine Learning algorithms and low-memory microcontrollers (ESP32-CAM). The uniqueness of this idea lies in the fact that Machine Learning is generally perceived as a processor-intensive task that requires high memory and storage. However, as seen in this chapter, one may soon realize how wrong this notion is with emerging technologies that are taking over the globe. This project portrays the successful implementation of a binary colour classification model on the ESP32-CAM with 68% accuracy post-training result with a mere 15 images of each colour. Machine learning has increased over the years. Some applications include image classification, object detection, and question-answering. This work merely puts out awareness in this domain and is hopeful that dedicated efforts towards it can solve many industrial problems.&amp;nbsp;&lt;br&gt;
Integrated Chance of Success (iCOS): Convolving geological and geophysical evidence into a single prediction

Summary Applying our new Integrated Chance of Success (iCOS) workflow, geoscientists integrate geological and geophysical observations through a Bayesian framework guided by expectations and confidence. The Geological Chance of Success (GCOS) is utilized as the prior probability and modified using the DHI Chance of Success (DHICOS). The modification is guided by a relative weighting from a new innovative DHI Discernibility metric, which leverages expectations and confidence in geophysical observations. The updated framework allows for integration of all observations into a single Chance of Success (COS) value. Benchmarking studies indicate this new approach is 50–60% better at discrimination and has 30–40% improvement in accuracy over the individual systems, while also mitigating cognitive bias.
The influence of e-learning digital literacy on cognitive flexibility and learning flow in nursing students

Purpose: The purpose of this study was to identify the impact of cognitive flexibility and e-learning digital literacy on the learning flow of nursing students who had experienced e-learning. Methods: The research design for this study was a descriptive survey using convenience sampling. Data were collected using online questionnaires completed by 134 nursing students in A city and P city. The data were analyzed using percentages, mean values, standard deviations, Pearson‘s correlation coefficients, and multiple regression with SPSS for Windows version 22.0. Results: Positive correlations were found between learning flow and e-learning digital literacy (r = .43, p &lt; .001), between learning flow and cognitive flexibility (r = .52, p &lt; .001), and between e-learning digital literacy and cognitive flexibility (r = .65, p &lt; .001). In the multiple regression analysis, cognitive flexibility (β = .42, p &lt; .001) was a significant predictor that explained 27% of variance in learning flow. Conclusion: The results of this study show that cognitive flexibility is a factor influencing learning flow in nursing students. Based on the results of the study, educational programs aiming to improve learning flow should include methods that improve cognitive flexibility.
Green Economy with Blockchain and Microgrid

100 % Green economy is a dream to be realized by every economy in the world. Every economy is firm in its motives in terms of investing in Green Technology and enhancing its contribution to the Green Economy, strategically any nation that masters the art of maximizing its Green Energy productivity is going to be a crucial player in the world order. In this scenario, Blockchain comes in as a tool that can be positively exploited for mankind’s betterment, especially in the field of Energy Conservation. Microgrid system in this context is a revolutionary mechanism for opening up the market of energy exchange to the public and reducing energy wastage/consumption.&lt;br&gt;
Image Denoising by Deep Convolution Based on Sparse Representation

Noise filtering is a crucial task in digital image processing, performing the function of preprocessing. In this paper, we propose an algorithm that employs deep convolution and soft thresholding iterative algorithms to extract and learn the features of noisy images. The extracted features are acquired through prior and sparse representation theory for image reconstruction. Effective separation of the image and noise is achieved using an end-to-end network of dilated convolution and fully connected layers. Several experiments were performed on public images subject to various levels of Gaussian noise, in order to evaluate the effectiveness of the proposed approach. The results indicated that our algorithm achieved a high peak signal-to-noise ratio (PSNR) and significantly improved the visual effects of the images. Our study supports the effectiveness of our approach and substantiates its potential to be applied to a broad spectrum of image processing tasks.
Functional data clustering via information maximization

A novel method for clustering functional data is introduced that utilizes information maximization. The method employs unsupervised learning to develop a probabilistic classifier that maximizes the mutual information or squared loss mutual information between data points and their corresponding cluster assignments. A significant advantage of this method is that it involves only continuous optimization of model parameters, which is simpler than discrete optimization of cluster assignments and avoids the drawbacks of generative models. Unlike existing methods, this method does not necessitate the estimation of probability densities of Karhunen-Loeve expansion scores under different clusters and does not require the common eigenfunction assumption. The efficacy of the proposed method is demonstrated through simulation studies and real data analysis. Additionally, the technique permits out-of-sample clustering, and its performance is comparable to that of supervised classifiers.
Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, natural language processing, and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of machine learning experiments with a domain-specific textual data set while considering two research directions: a. prediction analysis and b. data balancing. Wrong prediction analysis and the features that contributed to misclassification, along with correct prediction analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel four-step methodology was proposed in this paper for the first time, which consists of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtained better results than data undersampling in imbalanced data sets, while hybrid approaches performed reasonably well.
Multi‐scale cross‐domain alignment for person image generation

Abstract Person image generation aims to generate images that maintain the original human appearance in different target poses. Recent works have revealed that the critical element in achieving this task is the alignment of appearance domain and pose domain. Previous alignment methods, such as appearance flow warping, correspondence learning and cross attention, often encounter challenges when it comes to producing fine texture details. These approaches suffer from limitations in accurately estimating appearance flows due to the lack of global receptive field. Alternatively, they can only perform cross‐domain alignment on high‐level feature maps with small spatial dimensions since the computational complexity increases quadratically with larger feature sizes. In this article, the significance of multi‐scale alignment, in both low‐level and high‐level domains, for ensuring reliable cross‐domain alignment of appearance and pose is demonstrated. To this end, a novel and effective method, named Multi‐scale Cross‐domain Alignment (MCA) is proposed. Firstly, MCA adopts global context aggregation transformer to model multi‐scale interaction between pose and appearance inputs, which employs pair‐wise window‐based cross attention. Furthermore, leveraging the integrated global source information for each target position, MCA applies flexible flow prediction head and point correlation to effectively conduct warping and fusing for final transformed person image generation. Our proposed MCA achieves superior performance on two popular datasets than other methods, which verifies the effectiveness of our approach.
The Paradox of Democracy: Free Speech, Open Media, and Perilous Persuasion. By ZacGershberg and SeanIlling. Chicago, IL: University of Chicago Press. 2022. $20.00.

Political PsychologyEarly View Book Review The Paradox of Democracy: Free Speech, Open Media, and Perilous Persuasion. By Zac Gershberg and Sean Illing. Chicago, IL: University of Chicago Press. 2022. $20.00. B. Tyler Leigh, Corresponding Author B. Tyler Leigh [email protected] orcid.org/0000-0002-1396-0778 Annenerg School for Communication at University of Pennsylvania University of Pennsylvania School of Arts and SciencesSearch for more papers by this author B. Tyler Leigh, Corresponding Author B. Tyler Leigh [email protected] orcid.org/0000-0002-1396-0778 Annenerg School for Communication at University of Pennsylvania University of Pennsylvania School of Arts and SciencesSearch for more papers by this author First published: 24 May 2023 https://doi.org/10.1111/pops.12902Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL References de Tocqueville, A. ([1835] 1945). Chapter XV: Unlimited power of the majority in the United States, and its consequences. In P. Bradley, H. Reeve & F. Bowen (Eds.), Democracy in America. Vintage Books. Gershberg, Z., & Illing, S. D. (2022). The paradox of democracy: Free speech, open media, and perilous persuasion. The University of Chicago Press. Guess, A. M., Barberá, P., Munzert, S., & Yang, J. H. (2021). The consequences of online partisan media. Proceedings of the National Academy of Sciences of the United States of America , 118(14), 1– 8. Ingber, S. (1984). The marketplace of ideas: A legitimizing myth. Duke Law Journal , 1984(1), 1. Levendusky, M. (2013). Why do partisan media polarize viewers? American Journal of Political Science , 57(3), 611– 623. Mill, J. S. ([1859] 2002). Chapter II: Of the liberty of thought and discussion. In P. Negri & K. Casey, (Eds.), On liberty, 3rd ed. Dover Publications. Orben, A. (2020). The Sisyphean cycle of technology panics. Perspectives on Psychological Science , 15(5), 1143– 1157. https://journals-sagepub-com.proxy.library.upenn.edu/doi/full/10.1177/1745691620919372 Reeves, B., & Mutz, D. (2005). The new videomalaise: Effects of televised incivility on political trust. American Political Science Review , 99(1), 1– 15. Thurlow, C. (2006). From statistical panic to moral panic: The metadiscursive construction and popular exaggeration of new media language in the print media. Journal of Computer-Mediated Communication , 11(3), 667– 701. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
Unethical but not illegal! A critical look at two-sided disinformation platforms: Justifications, critique, and a way forward

Crowdsourced disinformation represents a two-sided-market model wherein a platform organizer orchestrates the interaction between disinformation requesters and crowdworkers for a fee. Academic research and industry reports demonstrate that the disinformation business is thriving and that its consequences can be severe; however, research on this topic has focused mainly on developing technical methods to detect disinformation, while leaving the social aspects of the phenomenon unaddressed. In particular, very little is known about the discursive tactics that platforms apply to justify disinformation-service offerings such that these appear acceptable to potential customers. Taking a critical approach to the topic, the paper examines how platform organizers justify their disinformation services and to what extent the justifications given are valid. These questions are addressed via a unique dataset from 10 crowdsourcing platforms specializing in social-media–based reputation management. Drawing on the lens of accounts, the analysis suggests that these platforms employ six means of justification for persuasion purposes: the “claim of entitlement,” “defense of the necessity,” the “claim of ubiquity,” “language sanitization,” “appeal to professionalism,” and “appeal to codified rules.” Critical discourse analysis scrutinizing these accounts against the validity claims of comprehensibility, truth, sincerity, and legitimacy indicates that they cannot be considered valid. The paper discusses the implications of the findings and offers several recommendations designed for improving the status quo.