Reviewerly/exHarmony huggingface dataset:
Analysis of the effectiveness of the implementation of digital technologies in the educational process of medical HEIs: challenges, optimization

The COVID-19 pandemic has led to the more intensive use of digital technologies in higher medical education. The purpose of the study was to analyze the effectiveness of the implementation of digital technologies in the educational process of Ukrainian medical universities, to identify the current challenges e-learning faces, and to propose potential ways of optimization. General scientific methods (for example analysis, synthesis) specific pedagogical methods (specification, abstraction, comparison forecasting) were used for the research. The results outline the main advantages of using digital technologies in the medical higher school system. Attention is also drawn to the main challenges that have a certain negative impact on the educational process. Among the latter, problems with the practical training of future doctors were noted since distance learning does not provide an opportunity to fully implement skills building component. The results also highlight possible ways to solve the problems of using digital technologies in medical higher education institutions. It is proposed to introduce to use not only distance learning technologies, but to combine them with the traditional approach and independent learning. It is emphasized about further studies, as empirical measurements of the effectiveness of distance education in the medical field. And, this effectivenessstill needs to be ultimately proven.
Synthesizing Quantum-Circuit Optimizers

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing . (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.
Critical Discourse Analysis in the Education Community to Respond the Hoax Based on Technology and Information

The research looked at how people in the Education community responded to the hoax phenomenon from the point of view of technology and information. This research aims to improve the educational component's ability to analyse different kinds of information or news. This will make it less likely that fake information will be used as a talking point or a guide. The method used is qualitative with critical discourse analysis, where the program's implementation is through the ASSURE method. The number of respondents is 60 consisting of teachers, parents and students in the sixth grade of an elementary school in Medan city. The technique used by researchers to gather data involves grouping various data, processing it in accordance with the aim to be recorded, and then deconstructing it using an essential conversation investigation preparation software created using the ASSURE approach. The technique of analyzing data was by implementing the six stages in the ASSURE model, which represent three other stages: pre-program, program implementation and post-program. The results show that critical discourse analysis through the basic conversation investigation program using the ASSURE method has proven to be effective and can be used to prevent the spread of hoax information that can cause various disputes.
From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets

Computer vision is a "data hungry" field. Researchers and practitioners who work on human-centric computer vision, like facial recognition, emphasize the necessity of vast amounts of data for more robust and accurate models. Humans are seen as a data resource which can be converted into datasets. The necessity of data has led to a proliferation of gathering data from easily available sources, including "public" data from the web. Yet the use of public data has significant ethical implications for the human subjects in datasets. We bridge academic conversations on the ethics of using publicly obtained data with concerns about privacy and agency associated with computer vision applications. Specifically, we examine how practices of dataset construction from public data-not only from websites, but also from public settings and public records-make it extremely difficult for human subjects to trace their images as they are collected, converted into datasets, distributed for use, and, in some cases, retracted. We discuss two interconnected barriers current data practices present to providing an ethics of traceability for human subjects: awareness and control. We conclude with key intervention points for enabling traceability for data subjects. We also offer suggestions for an improved ethics of traceability to enable both awareness and control for individual subjects in dataset curation practices.
Differentiable RandAugment: Learning Selecting Weights and Magnitude Distributions of Image Transformations

Automatic data augmentation is a technique to automatically search for strategies for image transformations, which can improve the performance of different vision tasks. RandAugment (RA), one of the most widely used automatic data augmentations, achieves great success in different scales of models and datasets. However, RA randomly selects transformations with equivalent probabilities and applies a single magnitude for all transformations, which is suboptimal for different models and datasets. In this paper, we develop Differentiable RandAugment (DRA) to learn selecting weights and magnitudes of transformations for RA. The magnitude of each transformation is modeled following a normal distribution with both learnable mean and standard deviation. We also introduce the gradient of transformations to reduce the bias in gradient estimation and KL divergence as part of the loss to reduce the optimization gap. Experiments on CIFAR-10/100 and ImageNet demonstrate the efficiency and effectiveness of DRA. Searching for only 0.95 GPU hours on ImageNet, DRA can reach a Top-1 accuracy of 78.19% with ResNet-50, which outperforms RA by 0.28% under the same settings. Transfer learning on object detection also demonstrates the power of DRA. The proposed DRA is one of the few that surpasses RA on ImageNet and has great potential to be integrated into modern training pipelines to achieve state-of-the-art performance. Our code will be made publicly available for out-of-the-box use.
Decoding Quantum Tanner Codes

We introduce sequential and parallel decoders for quantum Tanner codes. When the Tanner code construction is applied to a sufficiently expanding square complex with robust local codes, we obtain a family of asymptotically good quantum low-density parity-check codes. In this case, our decoders provably correct arbitrary errors of weight linear in the code length, respectively in linear or logarithmic time. The same decoders are easily adapted to the expander lifted product codes of Panteleev and Kalachev. Along the way, we exploit recently established bounds on the robustness of random tensor codes to give a tighter bound on the minimum distance of quantum Tanner codes.
Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach

Purpose The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research. Design/methodology/approach In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF. Findings This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection. Research limitations/implications In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis. Originality/value Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.
A stereo spatial decoupling network for medical image classification

Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective spatial associations, and always extracts similar low-level features, resulting in redundancy of information. To solve these limitations, we propose a stereo spatial discoupling network (TSDNets), which can leverage the multi-dimensional spatial details of medical images. Then, we use an attention mechanism to progressively extract the most discriminative features from three directions: horizontal, vertical, and depth. Moreover, a cross feature screening strategy is used to divide the original feature maps into three levels: important, secondary and redundant. Specifically, we design a cross feature screening module (CFSM) and a semantic guided decoupling module (SGDM) to model multi-dimension spatial relationships, thereby enhancing the feature representation capabilities. The extensive experiments conducted on multiple open source baseline datasets demonstrate that our TSDNets outperforms previous state-of-the-art models.
Innovation of college pop music teaching in traditional music culture based on robot cognitive-emotional interaction model

Emotional computing and artificial psychology is a new research direction that has received increasing attention in the field of harmonious human-computer interaction and artificial intelligence and is also a new intersection of mathematics, information science, intelligence science, neuroscience, physiology, psychological science and other multidisciplinary intersection. The current problems and drawbacks in the teaching of popular music in colleges and universities, and the search for methods and measures to reform and innovate popular music education in colleges and universities are the difficulties of current music teaching work. In this paper, we try to apply a robot cognitive-emotional interaction model to college pop music teaching, and establish an emotional interaction model based on reinforcement learning with the help of cognitive-emotional computing of human-computer interaction, to be able to integrate emotional interaction in pop music teaching and to make an accurate emotional analysis of students' singing effect. Different from traditional music teaching methods, the robot-based cognitive-emotional interaction model established in this paper can establish an innovative teaching model for college pop music teaching and optimize the teaching effect.
Clustered Federated Learning in Heterogeneous Environment

Federated learning (FL) is a distributed machine learning framework that allows resource-constrained clients to train a global model jointly without compromising data privacy. Although FL is widely adopted, high degrees of systems and statistical heterogeneity are still two main challenges, which leads to potential divergence and nonconvergence. Clustered FL handles the problem of statistical heterogeneity straightly by discovering the geometric structure of clients with various data generation distributions and getting multiple global models. The number of clusters contains prior knowledge about the clustering structure and has a significant impact on the performance of clustered FL methods. Existing clustered FL methods are inadequate for adaptively inferring the optimal number of clusters in environments with high systems' heterogeneity. To address this issue, we propose an iterative clustered FL (ICFL) framework in which the server dynamically discovers the clustering structure by successively performing incremental clustering and clustering in one iteration. We focus on the average connectivity within each cluster and give incremental clustering and clustering methods that are compatible with ICFL based on mathematical analysis. We evaluate ICFL in experiments on high degrees of systems and statistical heterogeneity, multiple datasets, and convex and nonconvex objectives. Experimental results verify our theoretical analysis and show that ICFL outperforms several clustered FL baseline methods.
Para Cima y Pa’ Abajo: Building Bridges Between HCI Research in Latin America and in the Global North

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced in/about LATAM; a set of driving forces and tensions with a heavy reliance on diasporic dynamics; and a set of professional demands and associated structural limitations. We also offer a roadmap towards building connections across HCI communities, in an attempt to rebuild HCI as a pluriverse.
Jamming-as-exploration: Creating and Playing Games to Explore Gender Identity

Games can be powerful vehicles for gender identity exploration and self-reflection but are often subject to designers' biases including gender representation, limiting such opportunities. Using a game jam as a research-through-design method, alongside qualitative interviews with the creators, this paper explores how the process of creating games and the games themselves can facilitate exploration of and reflection on gender identity. We highlight aspects of identity people want to explore; how different game elements can support processes of exploring these, and what aspects are missing in games. Further, the process of creating and playing helped participants reflect on and reframe their understanding of gender regardless of their identity/experience. Finally, we reflect on the process of designing an inclusive jam around the topic of gender identity, which can be sensitive and divisive. Our work results in implications for the design of games and other potential tools for gender exploration.
A Common Language for Reporting Earthquake Intensities

Scientists are working together to establish a standardized international scale for measuring and reporting the intensities and impacts of earthquake shaking.
Learning Structural Co-occurrences for Structured Web Data Extraction in Low-Resource Settings

Extracting structured information from all manner of webpages is an important problem with the potential to automate many real-world applications. Recent work has shown the effectiveness of leveraging DOM trees and pre-trained language models to describe and encode webpages. However, they typically optimize the model to learn the semantic co-occurrence of elements and labels in the same webpage, thus their effectiveness depends on sufficient labeled data, which is labor-intensive. In this paper, we further observe structural co-occurrences in different webpages of the same website: the same position in the DOM tree usually plays the same semantic role, and the DOM nodes in this position also share similar surface forms. Motivated by this, we propose a novel method, Structor, to effectively incorporate the structural co-occurrences over DOM tree and surface form into pre-trained language models. Such structural co-occurrences help the model learn the task better under low-resource settings, and we study two challenging experimental scenarios: website-level low-resource setting and webpage-level low-resource setting, to evaluate our approach. Extensive experiments on the public SWDE dataset show that Structor significantly outperforms the state-of-the-art models in both settings, and even achieves three times the performance of the strong baseline model in the case of extreme lack of training data.
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation

Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains. The former fails to leverage users’ cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users’ domain-specific preferences. In this paper we propose , a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. incorporates two hyperedge-based modules, namely dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users’ scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections to improve user representations. Experiments on both public and production datasets verify the superiority of for MDR.
Blockchain-based decentralized trust management in IoT: systems, requirements and challenges

Abstract Internet of Things (IoT) vision has astoundingly transcended environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines, and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real-world IoT scenarios relying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM), which is a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in TM field for different classes of IoT applications. It can provide tamper-proof data by enabling more reliable trust information and integrity verification, ultimately enhancing its availability and privacy when storing and sharing information. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain-based decentralized trust management systems (BCDTMS) for IoT. The contributions of this study are threefold; first, we provide the comprehensive and comparative analysis of state-of-the-art BCDTMS devised for different IoT classes such as Internet of Medical of Things (IoMT), Internet of Vehicles (IoV), Industrial IoT (IIoT), and Social IoT (SIoT). To make it an extensive study, we perform a detailed assessment of the existing BCDTMS in the literature in the aspects of Blockchain and TM. Second, we present requirements for developing Blockchain-based TM systems for IoT, and third we have highlighted the challenges in the context of using Blockchain for TM in various IoT applications.
On Quantum-Assisted LDPC Decoding Augmented with Classical Post-processing

Utilizing present and futuristic Quantum Computers to solve difficult problems in different domains has become one of the main endeavors at this moment. Of course, in arriving at the requisite solution both quantum and classical computers work in conjunction. With the continued popularity of Low Density Parity Check (LDPC) codes and hence their decoding, this paper looks into the latter as a Quadratic Unconstrained Binary Optimization (QUBO) and utilized D-Wave 2000Q Quantum Annealer to solve it. The outputs from the Annealer are classically post-processed using simple minimum distance decoding to further improve the performance. We evaluated and compared this implementation against the decoding performance obtained using Simulated Annealing (SA) and belief propagation (BP) decoding with classical computers. The results show that implementations of annealing (both simulated and quantum) are superior to BP decoding and suggest that the advantage becomes more prominent as block lengths increase. Reduced Bit Error Rate (BER) and Frame Error Rate (FER) are observed for simulated annealing and quantum annealing, at useful SNR range - a trend that persists for various codeword lengths.