Reviewerly/exHarmony huggingface dataset:
Applying Incremental Learning to Post-editing Systems: Towards Online Adaptation for Automatic Post-editing Models

Despite the increasingly good quality of automatic translations, machine-translated texts require corrections. Automatic post-editing models have been introduced to perform these corrections without human intervention. However, no system has been able to fully automate the post-editing process. Moreover, while numerous translation tools benefit from translators’ input, human–computer interaction has been underexplored in post-editing. This study discusses automatic post-editing models and suggests that they could be improved in more interactive scenarios, as previously done in machine translation. While some attempts were made to update automatic post-editing models incrementally, this was mostly done using synthetic corpora, which is likely to affect the performance. To address this issue and as part of this project, automatic post-editing models trained in a traditional setting were developed and updated in both batch and online modes without using synthetic resources, with a view to analysing the performance of incremental adaptations in different systems, domains and language pairs. While the interaction with the translator was simulated, an interactive functionality allowing for dynamic post-editing was included for demonstration purposes. The results showed that none of the models was able to beat the baseline and that the online models systematically yielded a lower performance. Moreover, a human evaluation identified recurrent error patterns. These outcomes confirm the difficulties faced by the task of automatic post-editing. Based on the results, several recommendations are put forward for conducting further research, including experiments with more data (possibly synthetic corpora) and different environmental variables.
Using Pheromone Trail Algorithm to Model Analog Memory

This chapter proposes the use of pheromone trail algorithm to model how the human mind registers data into the memory in the brain. In this approach, each time data is registered, a representation in the memory is marked with an additive quantity of pheromone to represent the reinforcement in the memory. When a new data is registered, the pheromone of the previously registered data is evaporated to represent the fading in the memory. While this work is intended to support the modeling of human memory where data are stacked up on each other, this model is extended with the algorithms to register and recall data embedded in an overlaid manner to represent the analog memory of a theoretical quantum computer. Numerical simulations are provided to illustrate the concept and to demonstrate the workability of the algorithms.
Named entity recognition for natural language understanding using BERT model

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Sandeep Kumar, Arun Solanki; Named entity recognition for natural language understanding using BERT model. AIP Conf. Proc. 22 December 2023; 2938 (1): 040011. https://doi.org/10.1063/5.0181535 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Filtering context-aware collaborative for mobile users learning

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ammar Wisam Altaher, Sabah Khudhair Abbas, Abdullah Hasan Hussein; Filtering context-aware collaborative for mobile users learning. AIP Conf. Proc. 22 December 2023; 2977 (1): 020063. https://doi.org/10.1063/5.0182268 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Investigating the Security of Health-care Information in Iranian Hospitals in Confronting with Threatening Risks

BACKGROUND: Like other organizations, hospitals are at risk of information security threats. The implementation and use of various kinds of electronic health records and information systems challenge the privacy and security management of personal care as well as health-care organizations. OBJECTIVE: This survey aims to evaluate information security by focusing on the differences among hospitals based on their size and type. MATERIALS AND METHODS: We conducted a survey, as a cross-sectional study, in 2023. The chief information officers of 165 hospitals in Iran were invited to participate. Furthermore, we designed an online questionnaire based on the ISO/IEC 27002. The scores of the hospitals were analyzed for significant differences in terms of seven factors of this questionnaire with respect to the size and type of hospitals. RESULTS: The 165 participating hospitals had a score of &lt;55% of the maximum possible score (100%). The hospitals with more than 200 beds had the highest level of information security, and the lowest level of information security was far hospitals with 150–200 beds. In all studied hospitals, the highest score was related to the component “Backup and security zones,” and the lowest score was related to the component “Encryption and staging.” Even the analysis based on the number of beds did not change this result. Furthermore, the private and university hospitals were weaker than other hospitals in terms of “organization and risk management” and “protection against attacks.” CONCLUSION: All participating hospitals in this study in Iran had an average score. Therefore, due to the importance of confidentiality of information in the health-care system, it is essential to provide a secure platform for information retention in hospitals. The causes of these threats should also be identified and controlled before experiencing harmful effects. We thus suggest that managers of health-care information and information technology departments in hospitals take appropriate corrective measures in policy development, user training, access control, risk management, as well as physical standards and protection against attacks.
Design and implementation of electromechanical biomedical waste containers powered by solar energy with a smart controller

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Amal Hassan Raheem, Hatam Kareem Kadhom, Ahmed Abdulqader Hussein; Design and implementation of electromechanical biomedical waste containers powered by solar energy with a smart controller. AIP Conf. Proc. 22 December 2023; 2977 (1): 020011. https://doi.org/10.1063/5.0182272 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Audio reading system for blind persons, testing and evaluation

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Mohammed Ali Mohammed, Karim Q. Hussein, Mustafa Dhiaa Al-Hassani; Audio reading system for blind persons, testing and evaluation. AIP Conf. Proc. 22 December 2023; 2977 (1): 040015. https://doi.org/10.1063/5.0182173 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Unbudgeted Exchange Rate Differences in Ghanaian Government Financial Statements: a Financial Accountability Study

The study investigates the occurrence and implications of unbudgeted exchange rate differences within the financial statements of the Government of Ghana. It delves into the discrepancies between budgeted and actual expenses related to foreign exchange, aiming to identify contributing factors, assess their impact on financial reporting accuracy, and propose recommendations for effective management. The research adopts a mixed-methods approach encompassing document analysis, interviews, and surveys to comprehensively explore the phenomenon. Theoretical frameworks from financial management literature and empirical data collected from governmental officials, financial experts, and document reviews form the basis for analysis. Findings reveal recurrent instances of unbudgeted payments due to exchange rate fluctuations, highlighting economic uncertainties, procedural gaps, and policy delays as primary contributors. These discrepancies significantly impact financial reporting accuracy and stakeholder trust. The study proposes policy reforms, enhanced risk management strategies, and transparent reporting practices as imperative for addressing these challenges. The implications of this research extend to the enhancement of financial management practices within governmental institutions, fostering transparency, and rebuilding stakeholder confidence in Ghana's financial reporting systems.
Open Science

Developments around Open Science practices are contributing to faster scientific discoveries by encouraging scientists to work and share research outputs more openly. In recent years, efforts have been made by funders, researchers, publishers, global citizens, and institutions, to promote open science. UNESCO has made recommendations on how nations can develop policy frameworks to enable coordinated approaches towards open science adoption. As open science develops, there are barriers especially in the Global South where there are underdeveloped research infrastructures and inadequate funding sources. However, the future of open science is promising, with many initiatives across the globe advocating for more open research practices.
Blockchain Technology and Sustainability

Blockchain has been touted as an enabler for sustainable innovations across many industries. In this chapter, we scrutinize this presumed positive impact by first elaborating on the core concepts: blockchain and sustainability. We then outline features of blockchain that can potentially yield positive sustainability outcomes and list several practical examples. We end this paper with five straightforward questions that can be used to critically assess any claimed impact of blockchain on sustainability.
Blockchain-based Logistics 4.0: enhancing performance of logistics service providers

Purpose Technology is the lifeline for the logistics industry, and it has been immensely disrupted by the emerging blockchain technology. This paper has two main objectives. The first is to explore how the current blockchain technology can be implemented in the logistics industry with the aim of improving logistic services amongst the network of logistics service providers (LSPs). The second is to propose the development of a blockchain model for the small and medium logistics service supply chain. Design/methodology/approach A prototype blockchain-based logistics system has been created and tested in a case study with a real logistics company. The primary technologies for developing a blockchain model on the Hyperledger platform as well as how the system is designed based on the logistics service flow are explained. Findings The study has resulted in the successful implementation of the proposed prototype blockchain-based logistics system. In particular, the case company has managed to fully utilise the developed tracking and tracing system. Whilst utilising the prototype, the participants have been able to fulfil their responsibilities in an effective manner. The performance of LSPs has improved following the World Bank Logistics Performance Index (LPI) criteria. Originality/value This paper contributes to current research in the application of blockchain technologies in the domain of logistics and the supply chain to progress LSPs towards Logistics 4.0. The current frameworks for Logistics 4.0 and how blockchain as a disruptive technology revolutionises logistic services are reviewed. In addition, this paper highlights the benefits of blockchain technology that LSPs can leverage to further improve their performance based on the LPI criteria.
A critical (theory) data literacy: tales from the field

Purpose This paper aims to explore how critical digital and data literacies are facilitated by testing different methods in the classroom, with the ambition to find a pedagogical framework for prompting sustained critical literacies. Design/methodology/approach This contribution draws on a 10-year set of critical pedagogy experiments conducted in Denmark, USA and Italy, and engaging more than 1,500 young adults. Multi-method pedagogical design trains students to conduct self-oriented guided autoethnography, situational analysis, allegorical mapping, and critical infrastructure analysis. Findings The techniques of guided autoethnography for facilitating sustained data literacy rely on inviting multiple iterations of self-analysis through sequential prompts, whereby students move through stages of observation, critical thinking, critical theory-informed critique around the lived experience of hegemonic data and artificial intelligence (AI) infrastructures. Research limitations/implications Critical digital/data literacy researchers should continue to test models for building sustained critique that not only facilitate changes in behavior over time but also facilitate citizen social science, whereby participants use these autoethnographic techniques with friends and families to build locally relevant critique of the hegemonic power of data/AI infrastructures. Originality/value The proposed literacy model adopts a critical theory stance and shows the value of using multiple modes of intervention at micro and macro levels to prompt self-analysis and meta-level reflexivity for learners. This framework places critical theory at the center of the pedagogy to spark more radical stances, which is contended to be an essential step in moving students from attitudinal change to behavioral change.
An Empirical Study of Bugs in Eclipse Stable Internal Interfaces

The Eclipse framework is a popular and widely used framework that has been evolving for over a decade. The framework provides both stable interfaces (APIs) and unstable interfaces (non-APIs). Despite being discouraged by Eclipse, application developers often use non-APIs which cause their systems to fail when ported to new framework releases. Previous studies showed that applications using relatively old non-APIs are more likely to be compatible with new releases compared to the ones that used newly introduced non-APIs. Furthermore, from our previous study about the stability of Eclipse internal interfaces, we discovered that there exist 327K stable non-API methods as the Eclipse framework evolves. In the same study, we recommended that 327K stable non-API methods can be used by Eclipse interface providers as possible candidates for promotion to stable interfaces. However, since non-APIs are unsupported and considered to be immature i.e., can contain bugs, to this end there exist a need to first investigate the stable non-APIs for possible bugs before they can be promoted to APIs. In this study, we empirically investigated the stable non-API for possible bugs using Sonarqube software quality tool. We discovered that over 79.8% classes containing old stable non-APIs methods have zero bugs. Results from this study can be used by both interface providers and users as a starting point to analyze which interfaces are well tested and also estimate how much work could be involved when performing bug fixing for a given eclipse release.
Rise of blockchain technology in supply chain transformation in the post-pandemic era

Purpose Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up. Design/methodology/approach This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it. Findings The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented. Research limitations/implications Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact. Practical implications This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood. Originality/value In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.
The Next ‘Deep’ Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach

The existing body of literature indicates a growing interest in research pertaining to the influence of artificial intelligence (AI) on marketing strategies, processes, and practices. However, further studies are required to fully unravel its complete potential and the implications it holds for practical application. The aim of this special issue on “The Next ‘Deep’ Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach” is to explore the next frontiers and delve into the various facets of AI-driven marketing, shedding light on cutting-edge research and practical insights that can shape the future of the field. It also focuses on novel ways of using AI techniques to derive innovative insights that can streamline marketing processes and make businesses more effective. The papers herein contribute not only to the advancement of knowledge and understanding surrounding the utilisation of AI in marketing but also play a crucial role in establishing a renewed and revitalised research agenda.
On the effectiveness of graph data augmentation for source code learning

The methodology that employs deep learning to handle software engineering tasks, such as bug detection, is commonly referred to as source code learning. Given the inherent graph nature of source code, graph learning, bolstered by graph neural networks (GNNs), has seen an increasing adoption in the domain of source code learning. Similar to other contexts within deep learning, source code learning also relies on extensive high-quality training data, and the scarcity of such data has become a primary impediment that leads to performance bottlenecks. In practice, data augmentation is often used as a countermeasure to mitigate this issue, by synthesizing additional training data based on existing ones. However, most existing practice of data augmentation in source code learning is limited to simple program transformation methods, such as code refactoring, thus not sufficiently effective. In this work, in light of the graph nature of source code, we propose to apply the data augmentation methods used for graph-structured data in graph learning to the tasks of source code learning, and we conduct a comprehensive empirical study to evaluate whether such new data augmentation approaches bring better effectiveness, in terms of producing more accurate and robust models. Specifically, we evaluate four critical software engineering tasks and seven neural network architectures to assess the effectiveness of five data augmentation methods. Experimental results identify that, compared to the data augmentation-free training approach, the Manifold-Mixup method can significantly improve both the accuracy and robustness of the trained models of source code learning, for up to 1.60% and 4.09%, respectively.
Bridge the gap between fixed-length and variable-length evolutionary neural architecture search algorithms

&lt;abstract&gt;&lt;p&gt;Evolutionary neural architecture search (ENAS) aims to automate the architecture design of deep neural networks (DNNs). In recent years, various ENAS algorithms have been proposed, and their effectiveness has been demonstrated. In practice, most ENAS methods based on genetic algorithms (GAs) use fixed-length encoding strategies because the generated chromosomes can be directly processed by the standard genetic operators (especially the crossover operator). However, the performance of existing ENAS methods with fixed-length encoding strategies can also be improved because the optimal depth is regarded as a known priori. Although variable-length encoding strategies may alleviate this issue, the standard genetic operators are replaced by the developed operators. In this paper, we proposed a framework to bridge this gap and to improve the performance of existing ENAS methods based on GAs. First, the fixed-length chromosomes were transformed into variable-length chromosomes with the encoding rules of the original ENAS methods. Second, an encoder was proposed to encode variable-length chromosomes into fixed-length representations that can be efficiently processed by standard genetic operators. Third, a decoder cotrained with the encoder was adopted to decode those processed high-dimensional representations which cannot directly describe architectures into original chromosomal forms. Overall, the performances of existing ENAS methods with fixed-length encoding strategies and variable-length encoding strategies have both improved by the proposed framework, and the effectiveness of the framework was justified through experimental results. Moreover, ablation experiments were performed and the results showed that the proposed framework does not negatively affect the original ENAS methods.&lt;/p&gt;&lt;/abstract&gt;