Reviewerly/exHarmony huggingface dataset:
Bringing digital competence to the disciplines

How can upper secondary teachers accommodate the increasing demand for integrating digital competence in their disciplines? Research on digital competence typically operates independently from specific disciplines and may be difficult to translate into teaching practices. Based on a three-year action research project, the paper presents the results of a study of teacher’s understanding of digital technologies in connection to their disciplines. The study involves two rounds of interviews with 40 teachers. From the interviews, different perspectives emerge with 1) technology-driven, 2) pedagogy-driven, and 3) discipline-specific use of digital technologies. By further exploring discipline-specific use of digital technologies, digital competence is seen as expanding the disciplines where technology directly impacts and transforms subject areas of disciplines. Further, drawing from teacher experiences from completed courses, the paper develops a framework for accommodating the interplay between digital competence and the disciplines. The framework presents boundary concepts that are meant to bridge the gap between teaching practices within the disciplines and contributions from research. The framework is intended for teachers in developing their disciplines from digital competence. Finally, the paper discusses how expanding the disciplines through the lens of digital competence may call for moving across and possibly beyond the disciplines.
Brief study on the emergence and preliminary development of China’s marine higher education in the early and mid-20th century

Abstract China’s marine higher education originated in the early twentieth century and was led and promoted by a group of educators and marine scientists in the Republic of China. There are three sources of marine higher education, namely, National Shandong University, Xiamen University, and Fudan University, which, over time, have formed three unique paths of marine higher education in China. With the establishment of the People’s Republic of China, from 1949 to 1952, there was a reform in marine higher education, in which the teachers and students of the Oceanography Group of Fudan University and the Department of Oceanography of Xiamen University were merged into Shandong University, and the three sources were then merged into one. Among them, the students of the Oceanography Group of Fudan University were adjusted to Shandong University by policy, which opened the prelude to China’s ‘colleges and departments adjustment’. After the ‘colleges and departments adjustment’, the characteristics of marine discipline at Shandong University became increasingly prominent, which drove the development of China’s marine higher education from less to more vigorous. As a whole, it has formed the unique characteristics and patterns of the emergence and the initial development of marine higher education in China.
Real‐time route planning for low observable <scp>unmanned combat aerial vehicle</scp>

Abstract The next generation of low observable (LO) unmanned combat aerial vehicle (UCAV) with highly autonomy to implement a penetration mission requires advanced methods for flyable and safe route planning (i.e., respecting physical capability of vehicle and threat coverage by hostile air defense radars) at a real‐time manner. Currently, the main challenge of real‐time route planning for LO UCAV is to achieve computationally efficiency under dynamic (pop‐up/moving) threats by air defense radars. In this paper, a real‐time planning paradigm in compliance with complex penetration requirements is proposed, and a complete modeling of route planning for LO UCAV's penetration as an optimal control problem is designed. The paper at first devises a direct method to transform the optimal control problem into a nonlinear programming (NLP) problem and then solves the formulated NLP problem under a moving planning horizon. The proposed method can give computationally efficient route planning results for LO UCAV's penetration under multiple kinds of radar threats. Numerical test results based on F‐16 uninhabited platform demonstrate the effectiveness of the proposed method.
Multi‐exposure embeddings for graph learning: Towards high dynamic range image saliency prediction

Abstract Identifying saliency in high dynamic range (HDR) images is a fundamentally important issue in HDR imaging, and plays critical roles towards comprehensive scene understanding. Most of existing studies leverage hand‐crafted features for HDR image saliency prediction, lacking the capabilities of fully exploiting the characteristics of HDR image (i.e. wider luminance range and richer colour gamut). Here, systematical studies are carried out on HDR image saliency prediction by proposing a new framework to single out the contributions from multi‐exposure images. Specifically, inspired by the mechanism of HDR imaging, the method first utilizes graph neural networks to model the relations among multi‐exposure images and the tone‐mapped image obtained from an HDR image, enabling more discriminative saliency‐related feature representations. Subsequently, the saliency features driven by global semantic knowledge are aggregated from the tone‐mapped image through enhancing global context‐aware semantic information. Finally, a fusion module is designed to integrate saliency‐oriented feature representations originated from multi‐exposure images and the tone‐mapped image, producing the saliency maps of HDR images. Moreover, a new challenging HDR eye fixation database (HDR‐EYEFix) is created, expecting to further contribute the research on HDR image saliency prediction. Experiment results show that the method obtains superior performance compared to the state‐of‐the‐art methods.
Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services

Abstract Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.
Exploring English Vocabulary Learning of Vietnamese Secondary School Students with VoiceGPT Assistance

With the advent of AI chatbots, many teachers’ teaching practices of English as a foreign language have undergone many changes. Many of them have become accustomed to employing ChatGPT to assist their work, bringing many benefits and potential challenges that, to date, have yet to be fully tested in any aspect. Particularly, two notable research gaps involve how Vietnamese secondary school students use VoiceGPT, the Vietnamese version of ChatGPT, to assist them in learning new English words and how they perceive this support. The current case study aimed to address these gaps by employing a quasi-experimental design at Lam Son Secondary School in Ho Chi Minh City with the participation of ten sixth-grade students in two English-intensive classes. In this investigation, the teacher used the Presentation-Practice-Production teaching method to teach vocabulary to her students, who were randomly assigned into two groups with the same number of members in each group, and the data for analysis was collected from their writing samples and semi-structured interviews. The findings indicate that sixth-grade students had different ways of using VoiceGPT to help them learn English words. The participants with VoiceGPT assistance outperformed those without this A.I. support in terms of lexical performance in the writing productions on five topics surveyed. In addition, they expressed favorable attitudes toward VoiceGPT’s benefits, but some concerns were raised about login difficulties, vocabulary range, and long response time.
Blockchain for finance: A survey

Abstract As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point‐to‐point transactions, and decentralized autonomous organizations. Here, the authors focus on blockchain‐based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third‐party verification by using consensus‐based verification. The 12 most popular blockchain platforms are investigated and 6 platforms that are related to finance are elaborated on, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain‐based securities trading applications. Numerous practical applications of blockchain‐based securities trading are gathered and they are categorized into four distinct categories. For each category, a typical example is introduced and how blockchain contributes to solving the key problems faced by FinTech companies and researchers explained. Finally, interesting observations are provided ranging from mainstream blockchain‐based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.
Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction

Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity concepts. To address this, our paper proposes a two-stage mention-level framework based on an enhanced heterogeneous graph attention network for document-level relation extraction. Our framework employs two different strategies to model intra-sentential and inter-sentential relations between fine-grained entity mentions, yielding local mention representations for intra-sentential relation prediction and global mention representations for inter-sentential relation prediction. For inter-sentential relation prediction and inference, we propose an enhanced heterogeneous graph attention network to better model the long-distance semantic relationships and design an entity-coreference path-based inference strategy to conduct relation inference. Moreover, we introduce a novel cross-entropy-based multilabel focal loss function to address the class imbalance problem and multilabel prediction simultaneously. Comprehensive experiments have been conducted to verify the effectiveness of our framework. Experimental results show that our approach significantly outperforms the existing methods.
DeforestVis: Behaviour Analysis of Machine Learning Models with Surrogate Decision Stumps

Abstract As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model‐agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier‐to‐explain. Yet, rule sets can become very lengthy, with many if–else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one‐level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade‐off by incrementally generating more stumps, creating attribute‐based explanations with weighted stumps to justify decision making, and analysing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case‐by‐case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.
High capacity reversible data hiding in radiographic images with optimal bit allocation

Abstract This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method.
Modeling digital skills beyond the IT workforce: construct definition, measurement and impact on digitalization value

Purpose Information technology (IT) personnels’ technical, business and behavioral skills are critical enablers for generating IT value. In an increasingly digitalized working environment where non-IT employees participate in digital innovations, a focus on IT personnels’ skills only doesn’t meet researchers’ need for a framework to study digital skills and managers’ need to address digital skills challenges across an enterprise’s workforce. Nevertheless, the digital skills topic is complicated by conceptual ambiguity and a lack of theoretically derived and empirically validated model. The purpose of this study is to address this problem. Design/methodology/approach Theoretically, this study draws on human capital (HC) and resource-based view (RBV) theories. Empirically, it follows mixed method combining interviews and a survey. Findings The digital skills construct is a multidimensional second order reflective construct. While its development is influenced by an organization’s commitment and exposure to digitalization, it influences the value organizations obtain from digitalization. Research limitations/implications This study conceptualizes the digital skills construct, identifying technology agnostic subdimensions that are meaningful beyond a particular digital domain [information and communication technology (ICT), information, Internet, Inter of Things (IoT)] and establishing a valid measure. Other researchers can improve both the indicators of the existing four conceptually distinct and managerially recognizable workplace digital skills dimensions as well as testing new ones. Practical implications Managers can use the instrument to assess the extent to which their non-IT workforces are equipped with digital skills and get strategic insights for specific interventions such as upskilling or buying in skills. Originality/value The main theoretical contribution of the paper is the conceptualization and validation of the digital skills construct for the non-IT workforce. Furthermore, we provide a theoretical framework to explain the factors that could influence the development of digital skills and demonstrate the impact that digital skills have on selected digitalization value indicators. This contribution provides the foundation for investigating the drivers, outcomes and the relationship of digital skills to other constructs such as digital transformation, innovation and firm performance.
A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.
Evaluating end‐users' digital competencies and ethical perceptions of <scp>AI</scp> systems in the context of sustainable digital banking

Abstract In the pursuit of fostering sustainable digital banking (SDB), this study investigates the nexus between end‐users' digital competencies, perceptions of artificially intelligent systems, and sustainable practices. With a focus on addressing inclusivity barriers and promoting ethical considerations, expert interviews were employed to provide nuanced insights. The study's foundation rests on the significance of sustainable development goals, digital competencies, and artificial intelligence (AI) ethics in shaping the future of digital banking. An interview schedule was developed and distributed online to IT experts and researchers in the field of AI. The instrument examined the major challenges faced by customers in accessing digital banking, the expected timeline for the complete digitalization of the South African banking sector, customers' anticipated competency, and comfort levels with a 100% digital banking interface, and their perspectives on the overall impact of such a transition on humanity. The interview gathered participants' views on AI‐based banking systems that determine credit scores and creditworthiness. The study also included questions to obtain expert opinions on customers' digital capabilities, access to resources, and the impact of AI trust issues on financial service customers. The findings of this study provided valuable insights for stakeholders, including governments, NGOs, and financial institutions. Implications derived from expert perspectives illuminate strategies for enhancing digital competencies, addressing inclusivity hurdles, and ensuring ethical AI integration. This study contributes to the sustainable development discourse by offering actionable recommendations for stakeholders in the pursuit of a more inclusive, digitally competent, and ethically responsible digital banking landscape.
Navigating the technical analysis in stock markets: Insights from bibliometric and topic modeling approaches

In stock markets, technical analysis plays a vital role by offering valuable insights into price trends, patterns, and anticipated market movements, aiding investors in making well-informed decisions. This study employs bibliometric and topic modelling approaches on 589 English-language journal articles indexed in Scopus in the last two decades (from 2003 to 2023), exclusively focusing on technical analysis in stock markets. The keyword co-occurrence analysis identifies five topic clusters. The application of structural topic modelling also unravels five prominent thematic clusters, namely pattern-based forecasting, rule-based trading, algorithmic trading, techno-fundamental trading, and machine learning &amp;amp;amp; sentiment analysis. The topic of pattern-based forecasting involves researching the application of various patterns or models to predict stock prices. Rule-based trading concentrates on utilizing technical analysis tools to generate buy and sell signals, aiming for profitability. The algorithmic trading cluster explores the use of algorithms to systematically execute buy and sell actions, especially in high-frequency trading scenarios. Techno-fundamental trading investigates the integration of both fundamental and technical analysis in trading and investment decisions. Lastly, machine learning &amp;amp;amp; sentiment analysis focus on applying advanced machine learning techniques and sentiment analysis for predicting stock prices, highlighting the use of sophisticated methods in this domain. The three predominant topics in the dataset are &amp;quot;rule-based trading,&amp;quot; &amp;quot;machine learning &amp;amp;amp; sentiment analysis,&amp;quot; and &amp;quot;algorithmic trading&amp;quot; constituting 26.79%, 23.52%, and 21.11% of the dataset, respectively. These findings underscore the prominence and significance of these themes within the context of the research domain.