Reviewerly/exHarmony huggingface dataset:
Computational empowerment of children: Design research on empowering and impactful designs by children

Prioritizing children’s empowerment in and through design has been on the agenda of child–computer interaction (CCI) research for a long time. Recently, the notion of the computational empowerment of children has received attention. However, there are still open issues in our understanding and advocacy of it. A related development is the recent interest in the longer-term impacts of our work. Fast and furious participation of children in design sessions is considered inadequate. We should advocate for longer-term trajectories and possibilities for children to make changes that will influence our world. However, the literature is limited in addressing longer-term impacts. This study taps into these two research gaps and showcases how we have addressed the computational empowerment of children in a project tackling bullying at school through critical design and making. In this paper, we examine in detail the children’s designs and their trajectories from the viewpoint of empowerment and impact: whether and how these children’s designs show potential for the empowerment of those bullied and whether and how their designs have had an impact in the realm of digital technology development. Our study has interesting conceptual and methodological implications for CCI research and practice on the computational empowerment of children and on our design research practice.
NLP techniques for automating responses to customer queries: a systematic review

Abstract The demand for automated customer support approaches in customer-centric environments has increased significantly in the past few years. Natural Language Processing (NLP) advancement has enabled conversational AI to comprehend human language and respond to enquiries from customers automatically independent of the intervention of humans. Customers can now access prompt responses from NLP chatbots without interacting with human agents. This application has been implemented in numerous business sectors, including banking, manufacturing, education, law, and healthcare, among others. This study reviewed earlier studies on automating customer queries using NLP approaches. Using a systematic review methodology, 73 articles were analysed from reputable digital resources. The evaluated result offers an in-depth review of prior studies investigating the use of NLP techniques for automated customer service responses, including details on existing studies, benefits, and potential future study topics on the use of NLP techniques for business applications. The implications of the results were discussed and, recommendations made.
AI-Based Smart Sensing and AR for Gait Rehabilitation Assessment

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.
The Art and Science of Prompt Engineering: A New Literacy in the Information Age

AbstractAbstractThe novel discipline of prompt-engineering is a combination of artificial intelligence, linguistics, and user experience design. Crafting effective prompts for AI models like OpenAI's GPT can optimize the quality of generated output, especially in reference services. The article explains the CLEAR Framework—a guiding tool that incorporates principles of conciseness, logic, explicitness, adaptability, and reflectiveness in prompt crafting. Despite challenges including biases, ethical concerns, and keeping up with rapidly evolving AI capabilities, the field also presents opportunities for growth. The article concludes with a call to action for library professionals to embrace and master prompt-engineering as an essential skill.Keywords: Prompt engineeringartificial intelligenceChatGPTAI-assisted servicesgenerative AI
Special Forces Algorithm: A novel meta-heuristic method for global optimization

This paper proposes a novel swarm intelligence optimizer inspired by human behavior called the Special Forces Algorithm (SFA). SFA is inspired by the missions of modern Special Forces in real life. First of all, in the initial stage of algorithm iteration, the speed of light mechanism is proposed for simulating the UAV-assisted search of special forces, and the loss probability mechanism is proposed for simulating the loss of contact due to force majeure during the search of special forces. The algorithm can more fully explore the solution space and enhance the ability of an algorithm to jump out of a local optimum. Secondly, by simulating the combat tactics of special forces, the algorithm uses command parameters to control the three stages of large-scale search, transitional stage search and small-scale precise search, making the balance between the exploration stage and the exploitation stage more reasonable. Finally, SFA inspired by special forces operations are presented. To verify the effectiveness of the proposed algorithm, SFA is compared with 6 classical algorithms in 23 benchmark functions. In addition, the algorithm is applied to practical engineering problems. Experimental results show that SFA has shown great potential and competitive results. SFA can achieve good search performance and optimization accuracy based on a better balance of exploration and exploitation capabilities.
Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective

Neoteric biomedical, technological, and normative shifts have prompted care firms to establish clinical governance as a contrivance to assure high-quality service in an exceedingly intricate milieu. Web security is an epochal concern in the healthcare sector, although it has garnered scant attention since the inception of web applications. The necessity to provide adequate security for healthcare web applications (HWAs) cannot be exaggerated, as umpteen health agencies are contingent on them to carry out their operations. Every healthcare organization renders a humongous volume of data available online to practitioners, pharmacies, and patients. Researchers are continually endeavoring to ameliorate techniques to increase the security and longevity of HWAs. In this context, experts examined certain imperative security risks in HWAs to quantitatively evaluate them in the design phase and covered numerous facets of HWAs, along with their security attributes and risk factors. The authors have proposed a combined approach of fuzzy-based symmetric techniques, i.e., AHP-TOPSIS (Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution), for the assessment of alternative HWAs, leveraging the multi-criteria decision-making (MCDM) approach. Ten consecutive HWAs from local hospitals in Uttar Pradesh, India, have been taken to estimate the security risk, incorporating this methodology to evaluate the priority of weightage and the impact of security attributes. Henceforth, the findings and methodology employed in this study can assist security practitioners in identifying and prioritizing the most influential risk factors to secure HWAs and encourage them to develop revamped or novel methods.
Novel multiple-image encryption algorithm based on a two-dimensional hyperchaotic modular model

Current multiple-image encryption schemes usually require a fixed number or the same size of images. Therefore, this paper proposes a multiple-image encryption algorithm that can encrypt any number, size, and type of image. Firstly, we propose a two-dimensional chaotic model, which can generate multiple types of chaotic systems to solve the defects of some existing chaotic systems and reduce the design difficulty of chaotic systems. The generated chaotic systems are proven to have better performance through bifurcation diagrams, phase diagrams, Lyapunov exponent, and sample entropy analysis. Secondly, we fuse multiple images and then use SHA-512 to generate a secret key to increase resistance to plaintext attacks. We embed the size message of the original image into the fused image pixels, eliminating the need to transmit the size message separately from the secret key. Finally, we propose a simultaneous permutation and diffusion algorithm to improve security and efficiency. Experimental simulations and security analysis show that the proposed algorithm can encrypt multiple images of different sizes and types with good attack resistance and encryption efficiency.
Multiparameter quantum metrology and mode entanglement with spatially split nonclassical spin ensembles

We identify the multiparameter sensitivity of split nonclassical spin states, such as spin-squeezed and Dicke states spatially distributed into several addressable modes. Analytical expressions for the spin-squeezing matrix of a family of states that are accessible by current atomic experiments reveal the quantum gain in multiparameter metrology, as well as the optimal strategies to maximize the sensitivity. We further study the mode entanglement of these states by deriving a witness for genuine $k$-partite mode entanglement from the spin-squeezing matrix. Our results highlight the advantage of mode entanglement for distributed sensing, and outline optimal protocols for multiparameter estimation with nonclassical spatially-distributed spin ensembles.
Blockchain-assisted authenticated key agreement scheme for IoT-based healthcare system

IoT devices are widely used in smart hospitals, digital health, and automated pathology laboratories to generate a large amount of heterogeneous data. These devices, coupled with medical sensors, enable healthcare professionals to monitor patients’ health and medical device performance in real-time. The collected health data is transmitted to centralized cloud servers for analysis and diagnosis. However, the wireless communication channels used by these devices are vulnerable to security threats such as unauthorized access, denial-of-service attacks, and man-in-the-middle attacks. Existing solutions rely on a single trusted authority for computing and storage, which can lead to issues such as higher latency, centralization, and a single point of failure. To address these concerns, we propose a new protocol called Blockchain-based IoMT Authenticated Key Exchange (BIoMTAKE) that establishes a private/consortium blockchain-based distributed environment using Hyperledger Fabric. This protocol eliminates the need for a single trusted authority and ensures secure access to data generated by IoMT devices. Before sharing or accessing any data from the distributed healthcare system, the BIoMTAKE scheme establishes a secured shared session for authenticated devices to prevent unauthorized access. The proposed methodology has been subjected to formal security analysis using the Scyther tool and informal security analysis, which demonstrates its strong security. Additionally, our performance analysis using Hyperledger Fabric and cryptography libraries shows that the proposed scheme is computationally and communicatively efficient.
The Influence of Technological Factors on the Computer-Assisted Audit Tools and Techniques Usage during COVID-19

There is minimal level of use of Computer-Assisted Audit Tools and Techniques (CAATTs) in developing nations regardless of its importance to audit productivity and cost reduction, and this holds particularly true in the public sector entities’ internal audit departments. Accordingly, this article aims to explore how technological factors, such as relative advantage, complexity, compatibility, observability, and trialability, contribute to the use of CAATTs in Jordan’s public sector internal audit during the COVID-19 pandemic and the impact of the pandemic on the profession’s outcome. The study also seeks to evaluate how the use of these tools affects the effectiveness of internal auditing, with the IT knowledge of the auditors serving as a moderating variable. This study used 91 usable responses from the internal audit managers of Jordanian public sector institutions. The study used the Diffusion of Innovation (DOI) theory to develop the proposed research model. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study result indicated that technological factors, with the exception of complexity, had a positive and significant effect on CAATTs use in the public sector internal audit departments. Based on the findings, using CAATTs has a positive and significant effect on internal audit effectiveness and IT knowledge has a positive moderating effect on the relationship between CAATTs usage and internal audit effectiveness. Owing to the public sector significance to the economy of Jordan, the findings have implications for the internal audit profession, regulators, and decision-makers in proposing new legislation and regulations when it comes to internal audit. Further, through the lens of the social implications, this study proposed that CAATTs usage in public sector institutions can positively improve their capability to reach the role of internal audit in protective public funds and limiting corrupt practices in the public sector. The paper contributes to theory by providing insight into the effect of factors on the use of CAATTs in the public sector of Jordan. This study, to the best of the author’s knowledge, is the first study that has tackled the moderating role of auditors’ IT knowledge on the CAATTs use–internal audit effectiveness relationship in the public sector context.
Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.
Blockchain based secure medical data outsourcing with data deduplication in cloud environment

With the rapid growth of cloud computing, an increasing number of individuals and businesses are opting to store their vast data on the cloud in order to cut data maintenance costs and obtain more easy access. The cloud-based Electronic Health Records (EHR) sharing scheme has provided many benefits in recent years, but the centralization of the cloud raises challenges to privacy protection and data security. Blockchain technology is viewed as a possible solution to these challenges because of its unique properties of unforgeability, anonymity, and verifiability. Therefore, a blockchain based secure data outsourcing scheme is proposed in this paper. In the proposed scheme, each activity on outsourcing EHRs is recorded as a transaction on the public blockchain, authenticity is ensured by registration, and secure data access is ensured by keyword search. Initially, the EHR is encrypted by the proposed Enhanced Map Chaotic Encryption (EMCE) algorithm to ensure confidentiality. Then, data de-duplication is performed by the proposed Tri-Level Chunk Hashing (TL-CH) technique to eliminated duplicate records and reduce the storage space. Finally, the integrity of outsourced EHRs is validated by the Third-Party Auditor (TPA). The performance and security analysis show that the proposed scheme can provide solid security against various attacks, and the storage space is reduced.
MRT-Net: Auto-adaptive weighting of manipulation residuals and texture clues for face manipulation detection

With the growing popularity of social media and an outburst of malicious tampering methods, it has become easier than ever to mislead others and create disharmony in society by manipulating social media content. Hence, there is an urgent need to build robust manipulation detection systems. This manuscript proposes a novel end-to-end architecture that extracts discriminative “manipulation residuals” (MR) and “textural” (T) features for facial manipulation detection. Most multi-branch architectures suffer from a common flaw in their fusion stage, where they combine multi-domain features in equal proportion. This is disadvantageous since each feature may not be equally important in making the final prediction. MRT-Net solves this problem by implementing an auto-adaptive weighting mechanism to select an ideal proportion of manipulation residual and textural information that prove complementary to one another. Specifically, two weighting factors for the MR and T features, ∝1 and ∝2 are added as parameters to the proposed neural network and are updated automatically via backpropagation, thereby allowing MRT-Net to find the ideal mix of residual and textural information. Additionally, MRT-Net benefits from a channel attention mechanism that boosts its performance even further. MRT-Net achieves excellent performance on three public benchmark datasets, Deep Fake Detection Challenge (DFDC), CelebDF and FaceForensics++ (FF++). AUC scores achieved are 0.9964 on DFDC, 0.9921 on CelebDF, 0.9910 on FF++(DeepFake), 0.9974 on FF++(Face2Face), 0.9942 on FF++(FaceShifter), 0.9933 on FF++(FaceSwap) and 0.9662 on FF++(NeuralTextures). It also achieves accuracy scores of 0.9760 on DFDC, 0.9815 on CelebDF, 0.9670 on FF++(DeepFake), 0.9767 on FF++(Face2Face), 0.9611 on FF++(FaceShifter), 0.9676 on FF++(FaceSwap) and 0.9025 on FF++(NeuralTextures). These excellent results demonstrate MRT-Net's potency as it comfortably outperforms other state-of-the-art face manipulation detection methods.
Postdigital/More-Than-Digital: Ephemerality, Seclusion, and Copresence in the University

Arguably, the genealogy of the term postdigital rests on the inseparability of the digital and the analog, that the concept that the digital and analog are intertwined as one inseparable entity, and that the digital will only be noticed by its absence, not its presence. This chapter explores the tensions inherent in these claims, arguing that the digital is theorised as ubiquitous and also occluded from direct view; resulting in the postdigital, in which the digital as a presence seems to be imagined as an entity outside of direct perception. Interrogating the concept of being intertwined and the nature of twine, I will argue that this commonly-used metaphor is in fact flawed, and does not provide sufficient theoretical purchase on the nature of the relationship between the digital and analog. I consider the concept of the network and the meshwork, arguing that neither of these concepts are adequate when seeking to capture the nature of the more-than-digital in the university. I argue for the importance of ephemerality, seclusion and copresence as fundamental elements of being and entanglement in the university; aspects of which which have hitherto been neglected in theories of the postdigital. I conclude by proposing the concept of fugitive practices.
Foreground–Background Contrastive Learning for Few-Shot Remote Sensing Image Scene Classification

Few-shot learning (FSL) aims to train a model with limited samples for identifying novel category samples. As for remote sensing images, complex backgrounds may lead to large intra-class differences, and the number of labeled samples is quite smaller than that of large datasets, which both influence the classification performance. To solve these issues, a foreground-background contrastive learning (FBCL) is proposed for few-shot remote sensing image scene classification. Specifically, a foreground-background separation module is proposed to separate features between objects and background with supervised contrastive learning, which aims to improve the ability to distinguish foreground and background regions of remote sensing images. Moreover, a channel weight allocator is proposed to balance features of different dimensions, which can take full advantage of remote sensing image information. Experiments on three remote sensing datasets prove that the proposed few-shot method is able to produce superior classification results than other related approaches.
Generative AI for Learning: Investigating the Potential of Learning Videos with Synthetic Virtual Instructors

Recent advances in generative artificial intelligence (AI) have captured worldwide attention. Tools such as Dalle-2 and ChatGPT demonstrate that tasks previously thought to be beyond the capabilities of AI may now augment the productivity of creative media in various new ways. To date, there is limited research investigating the real-world educational value of AI-generated media. To address this gap, we examined the impact of using generative AI to create learning videos with synthetic virtual instructors. We took a mixed-method approach, randomly assigning adult learners (n = 83) into one of two micro-learning conditions, collecting pre- and post-learning assessments, and surveying participants on their learning experience. The control condition included a traditionally produced instructor video, while the experimental condition included an AI-generated learning video with a synthetic virtual instructor. Learners in both conditions demonstrated significant improvement from pre- to post-learning (p <.001), with no significant differences in gains between the two conditions (p = .80), and no qualitative differences in the perceived learning experience. These findings suggest that AI-generated learning videos have the potential to be a viable substitute for videos produced via traditional methods in online educational settings, making high quality educational content more accessible across the globe.