Reviewerly/exHarmony huggingface dataset:
Features of lexical complexity: insights from L1 and L2 speakers

We discover sizable differences between the lexical complexity assignments of first language (L1) and second language (L2) English speakers. The complexity assignments of 940 shared tokens without context were extracted and compared from three lexical complexity prediction (LCP) datasets: the CompLex dataset, the Word Complexity Lexicon, and the CERF-J wordlist. It was found that word frequency, length, syllable count, familiarity, and prevalence as well as a number of derivations had a greater effect on perceived lexical complexity for L2 English speakers than they did for L1 English speakers. We explain these findings in connection to several theories from applied linguistics and then use these findings to inform a binary classifier that is trained to distinguish between spelling errors made by L1 and L2 English speakers. Our results indicate that several of our findings are generalizable. Differences in perceived lexical complexity are shown to be useful in the automatic identification of problematic words for these differing target populations. This gives support to the development of personalized lexical complexity prediction and text simplification systems.
Upholding Banker’s Duty of Confidentiality in Tanzania: an Examination of Legislative and Judicial Responses

Abstract Trust and confidence are the foundations of a banker-customer relationship. As a result, any information customers submit to their bankers is expected to be kept confidential. This expectation can only be met if the relevant legislative frameworks are in place to ensure that bankers are held accountable for their confidentiality agreements. While there is legislation in place to protect the privacy of customer information in Tanzania, a growing number of laws, notably those dealing with crime prevention and control, have been enacted, with the consequence of undermining bankers’ confidentiality obligations. Therefore, this article examines how recent legislative and judicial developments have addressed the confidentiality obligations imposed on bankers. In general, it has been observed and established that the protection of customers’ information provided by banking laws is usually degraded by other laws, particularly those pertaining to crime prevention, hence weakening banker’s duty of confidentiality.
A Large-Class Few-Shot Learning Method Based on High-Dimensional Features

Large-class few-shot learning has a wide range of applications in many fields, such as the medical, power, security, and remote sensing fields. At present, many few-shot learning methods for fewer-class scenarios have been proposed, but little research has been performed for large-class scenarios. In this paper, we propose a large-class few-shot learning method called HF-FSL, which is based on high-dimensional features. Recent theoretical research shows that if the distribution of samples in a high-dimensional feature space meets the conditions of compactness within the class and the dispersion between classes, the large-class few-shot learning method has a better generalization ability. Inspired by this theory, the basic idea is use a deep neural network to extract high-dimensional features and unitize them to project the samples onto a hypersphere. The global orthogonal regularization strategy can then be used to make samples of different classes on the hypersphere that are as orthogonal as possible, so as to achieve the goal of sample compactness within the class and the dispersion between classes in high-dimensional feature space. Experiments on Omniglot, Fungi, and ImageNet demonstrate that the proposed method can effectively improve the recognition accuracy in a large-class FSL problem.
Experimenting with Training a Neural Network in Transkribus to Recognise Text in a Multilingual and Multi-Authored Manuscript Collection

This work aims at developing an optimal strategy to automatically transcribe a large quantity of uncategorised, digitised archival documents when resources include handwritten text by multiple authors and in several languages. We present a comparative study to establish the efficiency of a single multilingual handwritten text recognition (HTR) model trained on multiple handwriting styles instead of using a separate model for every language. When successful, this approach allows us to automate the transcription of the archive, reducing manual annotation efforts and facilitating information retrieval. To train the model, we used the material from the personal archive of the Dutch glass artist Sybren Valkema (1916–1996), processing it with Transkribus.
A Multimodal Virtual Reality Inventory System

Applications involving virtual reality (VR), especially VR-based video games, integrate multiple gameplay mechanics. One such mechanic is an inventory system, which is a way for a player to manage their items in an easy and convenient manner. This study aims to iterate upon previous studies and implementations of inventory management to produce a new inventory system framework for use in VR adventure and serious games, with a primary focus on improving ease of use, convenience, and accessibility for a more refined and more immersive experience. The framework allows for different modalities, including control via vision-based hand tracking and traditional controllers. The study finds that the inventory systems implemented using the framework were able to satisfy essential user requirements criteria and improve upon existing implementations found in related literature, indicating that the framework is functional. Additionally, the study also finds that vision-based hand tracking provides a similar user experience as traditional controllers in terms of immersion, despite some tracking inaccuracies experienced when utilizing the former modality.
Asymmetric Entanglement-Assisted Quantum MDS Codes Constructed from Constacyclic Codes

Due to the asymmetry of quantum errors, phase-shift errors are more likely to occur than qubit-flip errors. Consequently, there is a need to develop asymmetric quantum error-correcting (QEC) codes that can safeguard quantum information transmitted through asymmetric channels. Currently, a significant body of literature has investigated the construction of asymmetric QEC codes. However, the asymmetry of most QEC codes identified in the literature is limited by the dual-containing condition within the Calderbank-Shor-Steane (CSS) framework. This limitation restricts the exploration of their full potential in terms of asymmetry. In order to enhance the asymmetry of asymmetric QEC codes, we utilize entanglement-assisted technology and exploit the algebraic structure of cyclotomic cosets of constacyclic codes to achieve this goal. In this paper, we generalize the decomposition method of the defining set for constacyclic codes and apply it to count the number of pre-shared entangled states in order to construct four new classes of asymmetric entanglement-assisted quantum maximal-distance separable (EAQMDS) codes that satisfy the asymmetric entanglement-assisted quantum Singleton bound. Compared with the codes existing in the literature, the lengths of the constructed EAQMDS codes and the number of pre-shared entangled states are more general, and the codes constructed in this paper have greater asymmetry.
Impact of the Intranatal Edu Application on Skills for Assisting in Childbirth

It is necessary to change teaching strategies and media because of the persistently poor pass rate of students' laboratory abilities in intranatal procedures. One such strategy is the use of engaging, interactive, and entertaining learning materials, such as Android applications. The goal of developing the app Intranatal Edu was to help students become more proficient. This study aims to evaluate the impact of the Intranatal Edu Application on the birth control knowledge of nursing students. Quantitative experimental research with a single group pre- and post-testing is the kind of research being done here. With a sample size of 60 respondents, the population of this study consisted of enrolled students in the third semester of the undergraduate nursing study program. Because the p-value of 0.005 is less than the significance threshold of 0.05, the Wilcoxon H0 test findings can be disregarded. It can be inferred that there is a difference in the birth support skills scores in Stage II pre- and post-test. According to this study, using the Edu Intranatal Application can effectively improve students' skills
Association rule hiding using enhanced elephant herding optimization algorithm

Association rule hiding is an efficient solution that helps organizations to avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. Cuckoo Optimization Algorithm (COA) sanitizes the transaction database but this method has limitation due to its slow convergence and exploitation capabilities. Hence in this paper, Enhanced Elephant Herding Optimization Algorithm for Association Rule Hiding (EEHOA4ARH) is proposed for association rule hiding. In EEHOA, two core functions such as clan updating operator and separating operator are used for association rule hiding that also realizes the fast convergence and exploitation capabilities. Moreover, the searching strategy in COA4ARH for the selection of best solution is highly time consuming. To reduce the time consumption for the selection of best solution, a Crowding Distance (CD) concept is combined with EEHOA4ARH. By continuously updating the best elephant and replacing the worst elephant in the population, EEHOA4ARH-CD sanitizes the transaction database effectively. Thus the proposed EEHOA4ARH achieves the less computation time, fast convergence and better exploitation capabilities by using crowding distance. The experimental results prove the effectiveness of the proposed EEHOA4ARH–CD method in terms of hiding failure, lost rule and execution time with 44.66 s.
Education to Agile: Fostering Team Awareness with Essence

This paper explores the incorporation of Agile practices in our undergraduate courses leveraging the Essence approach, a meta-notation for describing software processes, roles, and best practices. Exposing students and young developers to the Agile mindset and related methods is important to let them to cope with the challenges of modern software development and digital transformations. Agile methods and practices can also help students to develop valuable soft skills such as communication, teamwork, and adaptability. Essence clarifies and guides several key Agile practices, thanks to guidelines and checklists, such as: team building, customization of Agile ceremonies, promoting the effectiveness of retrospectives, tool selection and configuration. We found that the usage of Essence helps students to develop critical thinking and a sense of ownership and responsibility related to their teamwork. They achieve a better understanding of what is expected of them, and, as a result, they are more motivated to achieve their goals.
Analyzing Scrum Team Impediments Using NLP

In this research, we focus on the impediments encountered by students in capstone projects following the Scrum methodology. Scrum meeting notes were collected in a dataset to permit Scrum roles and instructors to monitor progress and issues. We identified 9 categories of impediments in this dataset: Android, Coding Skills, Debugging, External Factors, Firebase/Database, Git/GitHub, Teamwork, Time Management, and UI/UX Design. We developed a Large Language Model (LLM) to classify these impediments. Natural Language Processing (NLP) has the potential to support software engineering processes. The novelty of this research is that it attempts to identify impediments faced by students' Scrum teams with AI and support students and instructors. The relevance of the approach was discussed with subject matter experts (SME) of the industry. The proposed model is useful in both the academic and industry settings, to identify on-the-fly areas that need attention and, if fixed, would increase team productivity.
The WEIRD governance of fact-checking and the politics of content moderation

In this article, we chart the conflicting standards of fact-checking outside Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries that shifted their focus from holding politicians to account to acting as content moderators. We apply reflexive thematic analysis to a set of interviews with 37 fact-checking experts from 35 organizations in 27 countries to catalog the pressures they face and their struggle with tasks that are increasingly different from the journalistic values underpinning the practice. We find that fact-checkers have to balance the number of checks across each side of the partisan divide, an exercise in “bothsidesism” to manage the expectations of partisan social media users; that they increasingly prioritize the checking of viral content; and that Meta’s third-party fact-checking program prevents them from holding local politicians to account. We conclude with a discussion of our findings and recommendations for content moderation outside WEIRD countries.
Effects of speaker types and L1 backgrounds on the linguistic complexity of learners’ writing

Abstract This study investigated the differences in linguistic complexity between different types of English users, including native speakers (NS), English as a foreign language (EFL) learners, and English as a second language (ESL) learners in terms of Kolmogorov complexity. Furthermore, we explored how first language backgrounds affect linguistic complexity. Our dataset contains 2272 argumentative essays produced by English NSs and upper‐intermediate learners from four ESL and six EFL countries/regions. Results showed that significant differences existed between the writings of NS, EFL, and ESL regarding overall and syntactic complexity. Specifically, the rank of overall complexity (NS &gt; ESL &gt; EFL) indicates that learners from countries/regions with higher exposure to English tend to produce overall more complex writings. Concerning syntactic complexity, EFL learners produce the most complex writings, while NS produces the least complex, indicating that essays written by EFL learners contain the most fixed word order patterns. In contrast, no significant difference was detected in morphological complexity among the NS, ESL, and EFL groups, suggesting that native and upper‐intermediate non‐NSs exhibit a similar range of morphological forms in their writings. Additionally, our results showed a larger effect of first language backgrounds over English speaker types on linguistic complexity, thus informing teachers to implement targeted writing instructions for learners from different countries.
Designing the Visual Gateways into Crime News: A Comparison of Youtube Thumbnails from Journalists and Non-Journalists

Thumbnails and link previews play an outsized role in determining which online content is shared, seen, and engaged with. But conventions for these vary depending on the platform and the content creator. Journalists and non-journalists alike use and sometimes design these thumbnails with often striking differences. On some news aggregators, like Apple News or Google News, the bulk of the thumbnails are photographs related in some way to the linked content. Yet, on other platforms, such as YouTube, the thumbnails are often bespoke designs that allow for more storytelling freedom and potentially more ethical risk compared to camera-based images. This study, informed by visual news values, uses the context of the 2022 stabbing homicide of four University of Idaho students to systematically examine all YouTube thumbnails related to the murder suspect in the first four days after he was publicly identified. In doing so, this study is able to contribute to our understanding of the visual presentation of a crime-related topic with limited source material and is also able to shed much-needed light on how journalistic practices and conventions compare to those of non-journalists in the selection and design of image thumbnails on YouTube.
Analysing Semi-Supervised ConvNet Model Performance with Computation Processes

The rapid development of semi-supervised machine learning (SSML) algorithms has shown enhanced versatility, but pinpointing the primary influencing factors remains a challenge. Historically, deep neural networks (DNNs) have been used to underpin these algorithms, resulting in improved classification precision. This study aims to delve into the performance determinants of SSML models by employing post-hoc explainable artificial intelligence (XAI) methods. By analyzing the components of well-established SSML algorithms and comparing them to newer counterparts, this work redefines semi-supervised computation processes for both data preprocessing and classification. Integrating different types of DNNs, we evaluated the effects of parameter adjustments during training across varied labeled and unlabeled data proportions. Our analysis of 45 experiments showed a notable 8% drop in training loss and a 6.75% enhancement in learning precision when using the Shake-Shake26 classifier with the RemixMatch SSML algorithm. Additionally, our findings suggest a strong positive relationship between the amount of labeled data and training duration, indicating that more labeled data leads to extended training periods, which further influences parameter adjustments in learning processes.
Terms of entanglement: a posthumanist reading of Terms of Service

Contemporary connected things entail ongoing relations between producers, end users, and other actors characterized by ongoing updates and production of data about and through use. These relations are currently governed by Terms of Service (ToS) and related policy documents, which are known to be mostly ignored beyond the required interaction of ticking a box to indicate consent. This seems to be a symptom of failure to design for effectively mediating ongoing relations among multiple stakeholders involving multiple forms of value generation. In this paper, we use ToS as an entrance point to explore design practices for democratic data governance. Drawing on posthuman perspectives, we make three posthuman design moves exploring entanglements, decentering, and co-performance in relation to Terms of Service. Through these explorations we begin to sketch a space for design to engage with democratic data governance through a practice of what we call revealing design that is aimed at meaningfully making visible these complex networked relations in actionable ways. This approach is meant to open alternative possible trajectories that could be explored for design to enable genuine democratic data governance.
Hybrid UNet transformer architecture for ischemic stoke segmentation with MRI and CT datasets

A hybrid UNet and Transformer (HUT) network is introduced to combine the merits of the UNet and Transformer architectures, improving brain lesion segmentation from MRI and CT scans. The HUT overcomes the limitations of conventional approaches by utilizing two parallel stages: one based on UNet and the other on Transformers. The Transformer-based stage captures global dependencies and long-range correlations. It uses intermediate feature vectors from the UNet decoder and improves segmentation accuracy by enhancing the attention and relationship modeling between voxel patches derived from the 3D brain volumes. In addition, HUT incorporates self-supervised learning on the transformer network. This allows the transformer network to learn by maintaining consistency between the classification layers of the different resolutions of patches and augmentations. There is an improvement in the rate of convergence of the training and the overall capability of segmentation. Experimental results on benchmark datasets, including ATLAS and ISLES2018, demonstrate HUT's advantage over the state-of-the-art methods. HUT achieves higher Dice scores and reduced Hausdorff Distance scores in single-modality and multi-modality lesion segmentation. HUT outperforms the state-the-art network SPiN in the single-modality MRI segmentation on Anatomical Tracings of lesion After Stroke (ATLAS) dataset by 4.84% of Dice score and a large margin of 40.7% in the Hausdorff Distance score. HUT also performed well on CT perfusion brain scans in the Ischemic Stroke Lesion Segmentation (ISLES2018) dataset and demonstrated an improvement over the recent state-of-the-art network USSLNet by 3.3% in the Dice score and 12.5% in the Hausdorff Distance score. With the analysis of both single and multi-modalities datasets (ATLASR12 and ISLES2018), we show that HUT can perform and generalize well on different datasets. Code is available at: https://github.com/vicsohntu/HUT_CT.
Analysis on the Relationship Between the Environment and Economy in the Three Gorges Area

Three Gorges Project is one of the most famos famous project, this hydroelectric station is the most extensive in the world. After completion of this hydropower station, it also had a very large impact on the environment and economy of the Three Gorges area, directly changing the ecological environment and economic development of the area. This article analyzes the impact on the local environment since the completion of Three Gorges Dam, and the economic advance of Three Gorges region. Work on Three Gorges Dam began in 1994 and has been completed in 2020. Results show that the environment in Three Gorges region is relatively harsh, augment in water levels in Three Gorges has had a significant impact, many residents have had to move out of their long-lived land, and some wild fish populations have also been reduced because it is difficult to return upstream to spawn. Tourism should be developed to revitalize the local economy. There are also some suggestions can effectively alleviate these problems such as designate better tourism policies.
Invention of the MP3 format as one of the most successful projects in the digital history of Germany

The creation of the famous format for storing and broadcasting audio data – mp3 solved at one time the problem of high-precision transmission of human speech via telephone lines. But soon a real revolution took place in the world of telecommunications – a fiber-optic cable and an ISDN digital communication network were created. The developers switched to another idea – efficient encoding and compression of audio signals. The scientist who first drew attention to the fact that optimal compression of audio content is impossible without taking into account the features of the human hearing aid was Karlheinz Brandenburg, the &amp;quot;progenitor&amp;quot; of the mp3 format.