Reviewerly/exHarmony huggingface dataset:
The attitude of academic staff towards Bitcoin

Purpose: Bitcoin, the most prominent among cryptocurrencies, is a peer-to-peer (“P2P”) electronic currency and payment system used based on mutual trust. The study aims to measure awareness of Bitcoin, which has recently become popular among investors with its rapidly increasing use. To this end, a questionnaire was applied to the participants to evaluate their opinions and preferences regarding Bitcoin. With this purpose in mind, a questionnaire was applied to the academics of Erzincan Binali Yıldırım University in Turkey. Methodology: In the process of analysing the obtained data, frequency analysis and the chi-square test method were used by using the SPSS 26.0 software package. Results: According to the results of the study, it was observed that 51.6% of the academics (159 persons) had knowledge of Bitcoin, and while 31.5% of the academics participating in the study (97 persons) considered they would buy Bitcoin within five years, 57% (176 persons) conceived the use of Bitcoin would increase within ten years. Despite all these positive attitudes, it was also observed that 45.5% (140 people) considered Bitcoin as unreliable and 51.6% (159 persons) would prefer gold instead of using Bitcoin. As for the opinion of academics, it was concluded that gold and other different investment tools would be preferred instead of Bitcoin despite its increasing use. Conclusion: This research is important as it is the first research study in the field, which means that no similar study has been conducted in Turkey before, and it is thus expected to greatly contribute to the literature within the context of the importance and originality of the study.
Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing

Table fact verification aims to check the correctness of textual statements based on given semi-structured data.Most existing methods are devoted to better comprehending logical operations and tables, but they hardly study generating latent programs from statements, with which we can not only retrieve evidences efficiently but also explain reasons behind verifications naturally.However, it is challenging to get correct programs with existing weakly supervised semantic parsers due to the huge search space with lots of spurious programs.In this paper, we address the challenge by leveraging both lexical features and structure features for program generation.Through analyzing the connection between the program tree and the dependency tree, we define a unified concept, operation-oriented tree, to mine structure features, and introduce Structure-Aware Semantic Parsing to integrate structure features into program generation.Moreover, we design a refined objective function with lexical features and violation punishments to further avoid spurious programs.Experimental results show that our proposed method generates programs more accurately than existing semantic parsers, and achieves comparable performance to the SOTA on the large-scale benchmark TABFACT.
Evaluation of prospective teachers’ digital literacy levels and mobile learning attitudes

This research aims to examine prospective teachers’ digital literacy levels and mobile learning attitudes. A correlational survey method was used to design the research. In addition, whether the digital literacy levels and mobile learning attitudes of prospective teachers differed significantly according to the variables of gender, grade level, department and mobile application usage time was examined. The research was carried out with 443 prospective teachers studying at the education faculty of a state university. The prospective teachers in the research were studying at the 1st, 2nd, 3rd and 4th grade levels and in 10 different departments. The research data were collected in the digital environment and the participants were determined by the maximum variation sampling method. The research data were collected by using a digital literacy scale and mobile learning attitude scale. In addition, gender, grade level, department and mobile application usage time variables were used. According to the research results, prospective teachers’ digital literacy levels and mobile learning attitudes are at a moderate level and there is a positive and significant relationship between them. In addition, the digital literacy levels of prospective teachers explains 35% of their mobile learning attitudes. The digital literacy levels of prospective teachers significantly differ according to the gender, grade level and department variables and mobile learning attitudes significantly differ according to the department variable.
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge From Knowledge Graph

Mapping structured data to a shared domain ontology is a key step in publishing semantic content on the Web. This problem is known as Relational-To-Ontology Mapping Problem (Rel2Onto). Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Most of the related work focuses on semantic annotation of source attributes. However, besides semantically annotating source attributes, it is challenging to explicitly infer the relationships between attributes. In this paper we improve previous work by Taheriyan et al. [4] using Subgraph Matching to take into account frequencies of candidate semantic models occurring in the domain knowledge graph used as background knowledge. Preliminary experiments demonstrate that our method achieves higher precision and recall than the state-of-the-art solutions in the difficult scenarios where only few historical mappings between domain ontology and data sources are available.
Ex-Google chief’s venture aims to save neglected science software

Schmidt Futures is creating the US$40-million Virtual Institute of Scientific Software to fund the maintenance of researcher-written code. Schmidt Futures is creating the US$40-million Virtual Institute of Scientific Software to fund the maintenance of researcher-written code.
An Elastic Software Architecture for Extreme-Scale Big Data Analytics

Abstract This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing.
Digital Flipbook-Based Teaching Material for Writing Poetry in Elementary School

This study was conducted to solve the problem of children's difficulties in writing poetry in fourth grade by using digital teaching materials to write poetry in a form of flipboo k . The purpose of this research is to design a flipbook-based digital teaching material product to facilitate learning to write poetry in elementary schools, especially in Grade IV. The method used was Educational Design Research (EDR) starting from analyzing needs according to problem identification and exploring what was found in elementary schools, design and construction of developed tools, to evaluation and reflection of developed tools. The results of developing digital teaching materials for writing poetry were content s that can be taught to fourth grade elementary school students including: (1) The concept of poetry, (2) Basic poetry, (3) Elements of poetry, (4) Looking for ideas, (5) Pouring ideas in the form of poetry that is supported by multimedia provided by flipbook so that the digital teaching material for writing poetry based on flipbook developed by researchers are suitable to use. Thus , digital teaching material for writing poetry in the form of a flipbook can meet the needs of learning to write poetry in elementary schools because there are multimedia such as animation, images, videos, and audio so that learning can be supported and more interesting by utilizing the sophistication of existing technology. The position of this digital teaching material is as an additional reference in learning literature in elementary schools.
Merging-ISP: Multi-exposure High Dynamic Range Image Signal Processing

High dynamic range (HDR) imaging combines multiple images with different exposure times into a single high-quality image. The image signal processing pipeline (ISP) is a core component in digital cameras to perform these operations. It includes demosaicing of raw color filter array (CFA) data at different exposure times, alignment of the exposures, conversion to HDR domain, and exposure merging into an HDR image. Traditionally, such pipelines cascade algorithms that address these individual subtasks. However, cascaded designs suffer from error propagation, since simply combining multiple steps is not necessarily optimal for the entire imaging task.This paper proposes a multi-exposure HDR image signal processing pipeline (Merging-ISP) to jointly solve all these subtasks. Our pipeline is modeled by a deep neural network architecture. As such, it is end-to-end trainable, circumvents the use of hand-crafted and potentially complex algorithms, and mitigates error propagation. Merging-ISP enables direct reconstructions of HDR images of dynamic scenes from multiple raw CFA images with different exposures. We compare Merging-ISP against several state-of-the-art cascaded pipelines. The proposed method provides HDR reconstructions of high perceptual quality and it quantitatively outperforms competing ISPs by more than 1 dB in terms of PSNR.
ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing

Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets. Code is available at https://github.com/creinders/ChimeraMix.
Investigating perceptions about risk of data breaches in financial institutions: A routine activity-approach

Data breaches in financial institutions could produce extensive damage to an organization's operations. Therefore, it is critical for organizations to identify and assess threats in their operational environment to be able to implement prevention and mitigation strategies. Applying Routine Activity Theory (RAT), this paper develops a risk assessment model using employees' perceptions of risks relative to potential breaches of sensitive data in their organizations. This paper empirically examines the roles of motivated offenders, suitable targets, and the influences of capable guardianship within the organization. Analyses of surveyed employees show that perceptions of value (using a multi-dimensional perspective), inertia, and accessibility of targeted sensitive data along with presence of guardians have an impact on assessment of risk about data breaches in financial institutions. The paper also extends RAT to account for the amount of information (both online and offline) available regarding the data influence the relationship between value of the sensitive data and suitability for data breach. Theoretical and practical implications are discussed.
Searching for Optimal Subword Tokenization in Cross-domain NER

Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA). The most popular UDA approaches focus on domain-invariant representation learning, trying to align the features from different domains into a similar feature distribution. However, these approaches ignore the direct alignment of input word distributions between domains, which is a vital factor in word-level classification tasks such as cross-domain NER. In this work, we shed new light on cross-domain NER by introducing a subword-level solution, X-Piece, for input word-level distribution shift in NER. Specifically, we re-tokenize the input words of the source domain to approach the target subword distribution, which is formulated and solved as an optimal transport problem. As this approach focuses on the input level, it can also be combined with previous DIRL methods for further improvement. Experimental results show the effectiveness of the proposed method based on BERT-tagger on four benchmark NER datasets. Also, the proposed method is proved to benefit DIRL methods such as DANN.
SGAT: Simplicial Graph Attention Network

Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches for heterogeneous graphs use metapaths to capture multi-hop interactions between nodes. Typically, features from non-target nodes are not incorporated into the learning procedure. However, there can be nonlinear, high-order interactions involving multiple nodes or edges. In this paper, we present Simplicial Graph Attention Network (SGAT), a simplicial complex approach to represent such high-order interactions by placing features from non-target nodes on the simplices. We then use attention mechanisms and upper adjacencies to generate representations. We empirically demonstrate the efficacy of our approach with node classification tasks on heterogeneous graph datasets and further show SGAT's ability in extracting structural information by employing random node features. Numerical experiments indicate that SGAT performs better than other current state-of-the-art heterogeneous graph learning methods.
Locally Normalized Soft Contrastive Clustering for Compact Clusters

Recent deep clustering algorithms take advantage of self-supervised learning and self-training techniques to map the original data into a latent space, where the data embedding and clustering assignment can be jointly optimized. However, as many recent datasets are enormous and noisy, getting a clear boundary between different clusters is challenging with existing methods that mainly focus on contracting similar samples together and overlooking samples near boundary of clusters in the latent space. In this regard, we propose an end-to-end deep clustering algorithm, i.e., Locally Normalized Soft Contrastive Clustering (LNSCC). It takes advantage of similarities among each sample's local neighborhood and globally disconnected samples to leverage positiveness and negativeness of sample pairs in a contrastive way to separate different clusters. Experimental results on various datasets illustrate that our proposed approach achieves outstanding clustering performance over most of the state-of-the-art clustering methods for both image and non-image data even without convolution.
Pre-editing English news texts for machine translation into Russian

The paper discusses the possible advantages of pre-editing English news texts for machine translation into Russian. Pre-editing is defined as a process of adapting source text in order to reach a better quality of machine translation. Two case studies were conducted: one in 2021 and the other one in 2022. During case studies texts from bbc.com were chosen, pre-edited and translated using neural machine translation systems. Analysing common pre-editing techniques and their impact on the result of machine translation in terms of certain error patterns we conclude that in most cases pre-editing helps to eliminate a number of errors, improve the overall quality of machine output and reduce the amount of time and efforts needed for post-editing machine translation. The conducted case study also showed that despite the fact, that machine translation systems are constantly developing and changing, it is possible to identify common peculiarities of machine translation regarding a certain style or type of text and certain language pair, analyse the error patterns and find the appropriate pre-editing techniques that will be applicable for the most of machine translation systems for many months and years. Pre-editing does not guarantee the high quality of the translation itself, but together with post-editing it allows reaching results similar or equal to human translation saving a translator’s time and efforts.
The effectiveness of socrative and kahoot to teach grammar to students with different interest

This study aimed at examining the effectiveness of socrative and kahootto teach grammar to students with different interest. The objectives of the study are, to analyze high and low interest studentsâ€™ score of grammar in order to explain the effectiveness of socrative, to analyze high and low interest studentsâ€™ score of grammar in order to explain the effectiveness of kahoot, to analyze the significant difference between the students with high interest after taught by using Socrative and Kahoot to teach grammar, to analyze the significant difference between the students with low interest after taught by using Socrative and Kahoot to teach grammar, to analyze how the interaction is among teaching, studentsâ€™ interest and grammar. It was an experimental research with 2x2 factorial design. I used questionnaire, observation and test. The data from questionnaire and grammar test is analyzed quantitatively using T-test and ANOVA.The results of this research showed that socrative to students with high and low interest (79.17 and 57.50) and kahoot to students with high and low interest (85.50 and 62.86) are effective. Mean score of kahoot multimedia was higher than socrative in teaching grammar. There was no significant interaction among multimedia, interest and studentsâ€™ grammar achievement..
T1 Weighted MR Brain Image Segmentation with Triangular Intuitionistic Fuzzy Set

Segmentation of medical image is a very important step in processing of image to help examination of diseases. The early detection of ailments from the normal diseases is essential for the physicist to stop and provide treatment. Increasing Cerebrospinal fluid in brain causes dementia which is an increasing mostly prevalent now days. Segmentation of brain images is a challenge due to the existence of noise and intensity in-homogeneity that creates hesitation in segmenting the tissues. This paper is about a fresh segmentation method that uses triangular membership function to distinguish the early regions of brain tissue with intuitionistic fuzzy set. The triangular membership function helps in identifying the initial clusters and regions and facilitates in the decline of the number of iterations needed for segmentation. The proposed method successfully determines the brain tissues avoiding local minima and need of initial clusters. Hence, outperforms the existing method with increased accuracy and reduced computation time
Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction

Abstract Deep learning-based approaches in histopathology can be largely divided into two categories: a high-level approach using an end-to-end model and a low-level approach using feature extractors. Although the advantages and disadvantages of both approaches are empirically well known, there exists no scientific basis for choosing a specific approach in research, and direct comparative analysis of the two approaches has rarely been performed. Using the Cancer Genomic Atlas (TCGA)-based dataset, we compared these two different approaches in microsatellite instability (MSI) prediction and analyzed morphological image features associated with MSI. Our high-level approach was based solely on EfficientNet, while our low-level approach relied on LightGBM and multiple deep learning models trained on publicly available multiclass tissue, nuclei, and gland datasets. We compared their performance and important image features. Our high-level approach showed superior performance compared to our low-level approach. In both approaches, debris, lymphocytes, and necrotic cells were revealed as important features of MSI, which is consistent with clinical knowledge. Then, during qualitative analysis, we discovered the weaknesses of our low-level approach and demonstrated that its performance can be improved by using different image features in a complementary way. We performed our study using open-access data, and we believe this study can serve as a useful basis for discovering imaging biomarkers for clinical application.
Adversarial Learning for Cross Domain Recommendations

Existing cross domain recommender systems typically assume homogeneous user preferences across multiple domains to capture similarities of user-item interactions and to provide cross domain recommendations accordingly. Meanwhile, the heterogeneity of user behaviors is usually not well studied and captured during the recommendation process, where users might have vastly different interests in different domains. In addition, previous models focus primarily on recommendation tasks between domain pairs, and cannot be naturally extended to serve for multiple domain recommendation applications. To address these challenges, we propose to utilize the idea of adversarial learning to intelligently incorporate global user preferences and domain-specific user preferences for providing satisfying cross domain recommendations. In particular, our proposed Adversarial Cross Domain Recommendation (ACDR) model first obtains the latent representations of global user preferences from their explicit feature information, and then transforms them into domain-specific user embeddings, where we take into account user behaviors and their heterogeneous preferences among different domains. By doing so, we address the differences among user representations in the domain-specific latent space while also preserving global user preferences, as we effectively segment the distributions of domain-specific user embeddings in the shared latent space. The convergence of our proposed model is theoretically guaranteed. The proposed ACDR model leads to significant and consistent improvements in cross domain recommendation performance over the state-of-the-art baseline models, which we demonstrate through extensive experiments on three real-world datasets. In addition, we show that the improvements are greater on those datasets that are smaller and more sparse, on those users that have fewer interaction records in the dataset, and when user interactions from more product domains are included in the cross domain recommendation model.