Reviewerly/exHarmony huggingface dataset:
Factors Influencing the Effectiveness of E-Learning in Healthcare: A Fuzzy ANP Study

E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical isolation, and limited availability of specialized training programs and instructors. These challenges hinder their access to e-learning opportunities and impede their professional development. To address this issue, a study was conducted to identify the factors that influence the effectiveness of e-learning in healthcare. A literature review was conducted, and two questionnaires were distributed to e-learning experts to assess primary variables and identify the most significant factor. The Fuzzy Analytic Network Process (Fuzzy ANP) was used to identify the importance of selected factors. The study found that success, satisfaction, availability, effectiveness, readability, and engagement are the main components ranked in order of importance. Success was identified as the most significant factor. The study results highlight the benefits of e-learning in healthcare, including increased accessibility, interactivity, flexibility, knowledge management, and cost efficiency. E-learning offers a solution to the challenges of professional development faced by healthcare professionals in remote or rural areas. The study provides insights into the factors that influence the effectiveness of e-learning in healthcare and can guide the development of future e-learning programs.
How to introduce quantum computers without slowing economic growth

To smooth the path of the quantum revolution, researchers and governments must predict and prepare for the traps ahead. To smooth the path of the quantum revolution, researchers and governments must predict and prepare for the traps ahead.
“What if?” in Probabilistic Logic Programming

Abstract A ProbLog program is a logic program with facts that only hold with a specified probability. In this contribution, we extend this ProbLog language by the ability to answer “What if” queries. Intuitively, a ProbLog program defines a distribution by solving a system of equations in terms of mutually independent predefined Boolean random variables. In the theory of causality, Judea Pearl proposes a counterfactual reasoning for such systems of equations. Based on Pearl’s calculus, we provide a procedure for processing these counterfactual queries on ProbLog programs, together with a proof of correctness and a full implementation. Using the latter, we provide insights into the influence of different parameters on the scalability of inference. Finally, we also show that our approach is consistent with CP-logic, that is with the causal semantics for logic programs with annotated with disjunctions.
Are Cryptocurrencies Really a Threat to the Financial Stability and Economic Growth? Evidence From the Cointegration Approach

The main purpose of this paper is to investigate whether the cryptocurrency market affects financial stability and economic growth of India. The study used quarterly data on bitcoin, financial stability, inflation rate, real GDP, economic volatility uncertainty, exchange rate, and market volatility index for the period 2015Q1-2021Q4. The robustness of the findings was confirmed by the fully modified OLS (FMOLS) and canonical cointegration regression (CCR). The study results demonstrated that an increase in cryptocurrency investments will affect the financial stability of India significantly. Each 1% increase in the cryptocurrency would reduce the financial stability by 5% approximately. However, there was a marginal effect of cryptocurrency on economic growth. The results also found that exchange rate volatility and inflationary pressure would also deteriorate the financial stability of the country. Furthermore, the results also identified positive and significant cointegration between economic growth and financial stability. Due to most transactions in the economy being done through the financial system, it is paramount for economic growth. Going forward, aggressive monetary policy tightening, volatility in capital flows and exchange rates, deanchoring of inflation expectations, faltering in the economic recovery, disruptions due to global supply chains and climate change will be the major risks to the financial stability and economic growth of India.
MI-YOLO: more information based YOLO for insulator defect detection

Insulators that connect high-voltage transmission lines may experience various faults due to long-term exposure to the natural environment, leading to safety degradation and reliability issues in power grids. Therefore, detecting defective insulators through daily maintenance and long-term overhaul is crucial. We propose an insulator defect detection method to achieve this objective, using a novel neural network named more information-you only look once (MI-YOLO). MI-YOLO includes several modifications compared to YOLOv5s: a skip connection module with down-sampling in the backbone, a spatial pyramid dilated convolution module in the neck, and a novel serial-parallel spatial-channel attention module in between. These changes enhance the feature abstraction process by providing more information (MI) through the YOLO-like structure, hence the name MI-YOLO. To evaluate the superiority of MI-YOLO, multiple experiments are performed. First, an ablation study demonstrates the effectiveness of novel substructures and their combinations compared to YOLOv5s. Then, MI-YOLO is compared to baseline structures, including faster R-CNN, SSD, and five YOLOs. Furthermore, the performances of MI-YOLO and 10 state-of-the-art object detection methods are evaluated. Lastly, a second dataset is adopted to evaluate the generalization property and heat maps are presented. The experimental results show that the new designs substantially improve the accuracy of insulator fault detection by abstracting more information from the features.
Optical phase retrieving employing differentiation of a couple of two-beam π/2 phase-shifted projected interference patterns

An approach to retrieve the 3D surface map of an estimated (i.e., digitally generated) object by projection of a couple of differentiated two-beam interference patterns is presented. A couple of two-beam interference patterns of a certain spatial frequency, shifted by a phase angle ( π /2), are produced. Each one of these patterns is differentiated with respect to the carrier’s phase angle. The four obtained interference patterns are projected onto two estimated objects. Each object’s phase variations are retrieved utilizing the definition of the mathematical differentiation of the intensity distribution of the projected patterns. The inclination of the projected interference pattern on the object’s topography caused a redistribution of the intensity according to Lambert’s “cos” law of illumination. This effect is considered when the object’s phase map is retrieved. The limitations of the presented approach are discussed.
Onboarding and offboarding in virtual reality: A user-centred framework for audience experience across genres and spaces

Virtual reality (VR) technology is an emerging medium of experience in many different public-facing entertainment and cultural contexts, such as immersive theatre, live performance, VR film festivals, gaming arcades, escape rooms, and museum exhibitions. The processes of ushering audience members or users into the virtual experience and out again, to which I refer here as ‘onboarding’ and ‘offboarding’, have been considered within some specific contexts, or on a case-by-case basis, but to date no systematised consideration of VR onboarding and offboarding has been produced. One reason for this is that ambiguities in disciplinary and practical definitions of immersion have obscured the relationship between VR technology and users. Clarification of this relationship results in clear evidence of a need for attention to onboarding and offboarding processes in public-facing contexts. In this paper, I define onboarding and offboarding, and present a framework for considering the onboarding and offboarding experiences of virtual reality audiences that helps stakeholders identify both their responsibilities to audiences and the best way to facilitate the immersive experience. This framework is based upon identifying experience goals, centred on the affordances of virtual reality and principles of immersion and presence, and utilises the Immersive Audience Framework developed by StoryFutures in its interdisciplinary research with immersive audiences since 2019.
Interval-valued functional clustering based on the improved Euclidean distance with application to air quality index

Interval function clustering is a statistical method used to classify functional data based on interval number similarity measurements. However, existing similarity measurements focus on measuring the similarity of the curves in terms of numerical distance. This ignores the changing characteristics of the curve shape, which may lead to unreasonable clustering results when clustering interval-valued functional data. To address this issue, an improved Euclidean-distance-based interval-valued functional clustering method is proposed in this study. By deducing a specific calculation formula for the Euclidean distance of the interval function under the basis function and derivative information, the absolute difference in the numerical value was reflected based on the distance of the basis function, and the curve shape difference was reflected based on the distance of the derivative function information. Furthermore, a similarity measurement method combining numerical distance and curve shape was constructed to reflect the change characteristics of the functional data more completely and improve the K-means clustering process. Finally, the air quality index of different cities is taken as an example to cluster, and the differences and variation characteristics of different types of air quality are analyzed, which verifies the effectiveness of the proposed method.
TSE-Tran: Prediction method of telecommunication-network fraud crime based on time series representation and transformer

Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience. Therefore, preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system. Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns. This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran, which integrates temporal representation and transformer architecture. The time-series data of telecommunication-network fraud occurrences were input into the TimesNet module, which maps the sequence data to a more precise feature representation tensor that accounts for both intra- and inter-cycle features. Subsequently, the data are fed into the transformer-encoder module for further encoding, capturing long-range dependencies in the time-series data. Finally, occurrences of future telecommunication network frauds are predicted by a fully connected layer. The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy. The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.
Background oriented schlieren image displacement estimation method based on global optical flow

Background oriented schlieren (BOS) technology is a recent non-contact optical diagnostic technology, which can be used for the visualization of variable density flow and the quantitative measurement of the refractive index field. The accuracy of BOS image displacement estimation directly determines the accuracy of refractive index field reconstruction. For BOS images, the optical path construction method, vibration and uneven illumination, blurring and defocusing effects, and the refocusing effect of volume refraction make it difficult for the BOS image sequence to satisfy the constant brightness assumption. In this study, the optical flow algorithm based on the assumption of a constant gradient in PIV is improved and applied to BOS image displacement estimation. A data item based on the assumption of constant brightness combined with the assumption of constant gradient is proposed. The first-order div-curl regularization constraint is used as the spatial smoothing condition, and the non-convex Charbonnier penalty function is used to determine the displacement field by the variational method to minimize the energy function. The proposed method is tested on the synthetic images obtained by two-dimensional turbulent direct numerical simulation (DNS), the BOS synthetic images with brightness changes, and the actual experimental BOS images of the flow field above a premixed butane/air combustion flame with different equivalence ratios. Compared with the current advanced optical flow algorithm, this method has higher accuracy and robustness. Overall, the proposed method can not only obtain more accurate results when the brightness of the BOS synthetic image and experimental image changes, but also helps in retaining the flow details such as small divergence and vorticity structure of the heat flow field while reducing the outliers.
Reasoning with belief functions over Belnap–Dunn logic

We design an expansion of Belnap--Dunn logic with belief and plausibility functions that allow non-trivial reasoning with inconsistent and incomplete probabilistic information. We also formalise reasoning with non-standard probabilities and belief functions in two ways. First, using a calculus of linear inequalities, akin to the one presented in~\cite{FaginHalpernMegiddo1990}. Second, as a two-layered modal logic wherein reasoning with evidence (the outer layer) utilises paraconsistent expansions of \L{}ukasiewicz logic. The second approach is inspired by~\cite{BaldiCintulaNoguera2020}. We prove completeness for both kinds of calculi and show their equivalence by establishing faithful translations in both directions.
Probability propagation rules for Aristotelian syllogisms

We present a coherence-based probability semantics and probability propagation rules for (categorical) Aristotelian syllogisms. For framing the Aristotelian syllogisms as probabilistic inferences, we interpret basic syllogistic sentence types A, E, I, O by suitable precise and imprecise conditional probability assessments. Then, we define validity of probabilistic inferences and probabilistic notions of the existential import which is required, for the validity of the syllogisms. Based on a generalization of de Finetti's fundamental theorem to conditional probability, we investigate the coherent probability propagation rules of argument forms of the syllogistic Figures I, II, and III, respectively. These results allow to show, for all three figures, that each traditionally valid syllogism is also valid in our coherence-based probability semantics. Moreover, we interpret the basic syllogistic sentence types by suitable defaults and negated defaults. Thereby, we build a bridge from our probability semantics of Aristotelian syllogisms to nonmonotonic reasoning. Then we show that reductio by conversion does not work while reductio ad impossibile can be applied in our approach. Finally, we show how the proposed probability propagation rules can be used to analyze syllogisms involving generalized quantifiers (like Most).
A bit-plane encryption algorithm for RGB image based on modulo negabinary code and chaotic system

This paper proposes a new color image encryption algorithm based on modulo negabinary code (MNBC-IEA), which treats a color pixel as one 24-bit unsigned integer rather than three 8-bit unsigned integers, and it makes the 24-bit-planes dependent on each other. We first provide a new coding scheme of unsigned integers called modulo negabinary code (MNBC), and we construct its encoding and decoding algorithm. Next, we use MNBC to design the substitution algorithm for color image encryption. MNBC-IEA consists of the enhanced MNBC, a random cyclic shift (RCS), and a diffusion procedure. The RCS procedure is composed of the RCSs in three directions, and it treats a color image as a cuboid. Hence, the permutation process can cause the 24-bit-planes to affect each other in addition to scrambling the bit positions. The enhanced MNBC can change the grayscale values via a process that encodes a 28-bit unsigned integer consisting of a 24-bit pixel value and a 4-bit key into the MNBC. The diffusion procedure includes exclusive OR and modulo arithmetic such that plaintext pixels, ciphertext pixels, and keystreams are related to each other. The final experiments indicate that our algorithm can encrypt a color image into a random binary sequence and resist various statistical, differential, and brute-force attacks.
Automating medical simulations

This study aims to explore speech as an alternative modality for human activity recognition (HAR) in medical settings. While current HAR technologies rely on video and sensory modalities, they are often unsuitable for the medical environment due to interference from medical personnel, privacy concerns, and environmental limitations. Therefore, we propose an end-to-end, fully automatic objective checklist validation framework that utilizes medical personnel's uttered speech to recognize and document the executed actions in a checklist format.Our framework records, processes, and analyzes medical personnel's speech to extract valuable information about performed actions. This information is then used to fill the corresponding rubrics in the checklist automatically.Our approach to activity recognition outperformed the online expert examiner, achieving an F1 score of 0.869 on verbal tasks and an ICC score of 0.822 with an offline examiner. Furthermore, the framework successfully identified communication failures and medical errors made by physicians and nurses.Implementing a speech-based framework in medical settings, such as the emergency room and operation room, holds promise for improving care delivery and enabling the development of automated assistive technologies in various medical domains. By leveraging speech as a modality for HAR, we can overcome the limitations of existing technologies and enhance workflow efficiency and patient safety.
Artificial Intelligence Application in Supply Chain Management in the Government Sector of Pakistan

As the world is becoming more digital, private companies are now using cutting-edge technologies to streamline their operations and increase public trust. In countries like Pakistan, where the government is in charge of most public services, technological adoption is premature for a number of reasons. This study identified the use of artificial intelligence (AI) in five different management components, such as demand management, procurement management, logistics management, disposal management, and risk management which contribute to enhancing overall supply chain management performance in the public sector. Research found that bureaucracy is the crucial adoption barrier that existed in Pakistan’s public distribution systems, with AI being the most popular technology.
Diffraction Block in Extended nn-UNet for Brain Tumor Segmentation

Automatic brain tumor segmentation based on 3D mpMRI is highly significant for brain diagnosis, monitoring, and treatment planning. Due to the limitation of manual delineation, automatic and accurate segmentation based on a deep learning network has a tremendous practical necessity. The BraTS2022 challenge provides many data to develop our network. In this work, we proposed a diffraction block based on the Fraunhofer single-slit diffraction principle, which emphasizes the effect of associated features and suppresses isolated features. We added the diffraction block to nn-UNet, which took first place in the BraTS 2020 competition. We also improved nn-UNet by referring to the solution proposed by the 2021 winner, including using a larger network and replacing the batch with a group normalization. In the final unseen test data, our method is ranked first for Pediatric population data and third for BraTS continuous evaluation data.
Unsupervised Anomaly Localization with Structural Feature-Autoencoders

Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the anomaly detection model generates a “normal” version of an input image, and the pixel-wise $$l^p$$ -difference of the two is used to localize anomalies. However, large residuals often occur due to imperfect reconstruction of the complex anatomical structures present in most medical images. This method also fails to detect anomalies that are not characterized by large intensity differences to the surrounding tissue. We propose to tackle this problem using a feature-mapping function that transforms the input intensity images into a space with multiple channels where anomalies can be detected along different discriminative feature maps extracted from the original image. We then train an Autoencoder model in this space using structural similarity loss that does not only consider differences in intensity but also in contrast and structure. Our method significantly increases performance on two medical data sets for brain MRI. Code and experiments are available at https://github.com/FeliMe/feature-autoencoder .
Advancements in Natural Language Processing for Text Understanding

Natural language processing (NLP) developments have made it possible for robots to read and analyze human language with astounding precision, revolutionizing the field of text understanding. An overview of current advancements in NLP approaches and their effects on text comprehension are provided in this abstract. It examines significant developments in fields including named entity identification, sentiment analysis, semantic analysis, and question answering, highlighting the difficulties encountered and creative solutions put forth. To sum up, recent developments in natural language processing have raised the bar for text comprehension. Deep learning models and extensive pre-training have changed methods including semantic analysis, sentiment analysis, named entity identification, and question answering. These developments have produced text comprehension systems that are increasingly precise and complex. However, issues with prejudice, coreference resolution, and contextual comprehension still need to be resolved. The future of NLP for text understanding has considerable potential with continuing study and innovation, opening the door for increasingly sophisticated applications in numerous sectors.