Reviewerly/exHarmony huggingface dataset:
The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%.
Multi‐Wavelength Quantum Light Sources on Silicon Nitride Micro‐Ring Chip

Abstract Multi‐wavelength quantum light sources are extremely desired in establishing communication links among multiple users for realizing quantum networks. Despite recent impressive advances, developing such a quantum light source with high quality remains challenging. Here a multi‐wavelength quantum light source using a silicon nitride micro‐ring with a free spectral range of 200 GHz is demonstrated. The generation of eight‐wavelength‐paired photon pairs is ensured in a wavelength range of 25.6 nm. With device optimization and noise‐rejecting filters, this source enables the generation of heralded single‐photons at a rate of 62 kHz with and the generation of energy‐time entangled photons with a visibility of in the Franson interferometer. These results, at room temperature and telecom wavelength, in a CMOS‐compatible platform, represent an important step toward integrated quantum photonic devices, which pave the way for realizing a large‐scale quantum network.
Few-Shot Specific Emitter Identification Using Asymmetric Masked Auto-Encoder

Specific emitter identification (SEI) based on radio frequency fingerprint (RFF) characteristics can be used to identify different transmitters, and the deep learning (DL)-based SEI methods have achieved good performance with sufficient samples. However, these methods are difficult to identify emitters when the labeled training samples are limited. Hence, we propose a few-shot SEI (FS-SEI) using asymmetric masked auto-encoder (AMAE) to solve the few-shot problem. Specifically, we use the sufficiently unlabeled training samples which refer as the source domain to drive the training process of AMAE to obtain an RFF extractor with good feature extraction performance on the source domain, and then the pre-trained RFF extractor together with a classifier is fine-tuned using limited labeled training samples which refer as the target domain. Simulation results show that the proposed AMAE-based FS-SEI method achieves state-of-the-art identification performance compared to other supervised and unsupervised methods on the LoRa dataset with 30 categories and WiFi dataset with 16 categories. The codes can be downloaded from GitHub: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YZS666/A-Method-for-Solving-the-FS-SEI-Problem</uri> .
Knowledge-guided Aspect-based Summarization

Contextualized pre-trained models, such as BERT [1] and BART [2], have shown great potential in various NLP tasks, pushing the state-of-the-art results to a new level. Although studies have shown that those pre-trained models have captured different kinds of knowledge due to the massive corpus they have been trained on [3], injecting task-specific external knowledge often shows further improvement [4]. Here we choose aspect-based abstractive summarization as a case study and explore two different ways to inject external knowledge into BART. One is through a knowledge graph, the other is through human-defined sequence-level scores. Experiment results show that both methods can get an improvement over vanilla BART.
Quantum computing of fluid dynamics using the hydrodynamic Schrödinger equation

Simulating fluid dynamics on a quantum computer is intrinsically difficult due to the nonlinear and non-Hamiltonian nature of the Navier-Stokes equation (NSE). We propose a framework for quantum computing of fluid dynamics based on the hydrodynamic Schr\"odinger equation (HSE), which can be promising in simulating three-dimensional turbulent flows in various engineering applications. The HSE is derived by generalizing the Madelung transform to compressible or incompressible flows with finite vorticity and dissipation. Since the HSE is expressed as a unitary operator on a two-component wave function, it is more suitable than the NSE for quantum computing. The flow governed by the HSE can resemble a turbulent flow consisting of tangled vortex tubes with the five-thirds scaling of energy spectrum. We develop a prediction-correction quantum algorithm to solve the HSE. This algorithm is implemented for simple flows on the quantum simulator Qiskit with partial exponential speedup.
Understanding learners' intention toward Metaverse in higher education institutions from a developing country perspective: UTAUT and ISS integrated model

Purpose The Metaverse is an immersive virtual world where individuals interact with each other using an avatar. The Metaverse is promised to offer several potentials for different sectors. While the Metaverse promotes innovative interaction between individuals (e.g. learners), there is a scarcity of knowledge on what influences its acceptance and use in higher educational institutions (HEIs), particularly in developing countries. Thus, this paper aims to identify the factors that affect the users' intentions toward Metaverse technology for educational purposes in the HEIs in Jordan. Design/methodology/approach A study model was created by incorporating variables from the unified theory of acceptance and use of technology (UTAUT) (performance expectancy, PE; effort expectancy, EE; social influence, SI; and facilitating conditions, FC) and information system success (ISS) model (user satisfaction, SAT; system quality, SQ; service quality, SVQ and information quality, IQ) with perceived curiosity (PC) and hedonic gratification (HG) constructs. Both PC and HG were added as context-related factors that could contribute to improving the applicability of UTAUT and ISS to a wide range of technology use contexts. Data were gathered from 441 Jordanian university students using an online questionnaire. Findings The analysis of the structural model revealed that users' behavioural intentions toward using Metaverse were significantly affected by user satisfaction (SAT), performance expectancy (PE), facilitating conditions (FC) and hedonic gratification (HG). Also, information quality (IQ) and services quality (SVQ) were revealed to have a significant influence on user satisfaction. Likewise, the results illustrated a significant impact of systems quality (SQ), information quality (IQ) and service quality (SVQ) on PE. Lastly, a significant effect of PC on the EE factor was found. Practical implications This study will shed light on a number of practical implications for developers, designers, service providers and decision-makers supporting the acceptance and adoption of the Metaverse technology in the educational context. Also, it will contribute to developing the right strategies, and Metaverse content which will draw the attention of learners. Originality/value As per the surveyed literature and the researcher's prior knowledge, this is one of the first studies that integrate the UTAUT model, ISS model and two contextual factors (PC and HG) besides testing the proposed model in a developing country (Jordan). This paper is one of the few in which the individuals' acceptance behaviour of Metaverse technology was examined in the HEIs context.
Unlocking the Potential: A Comprehensive Evaluation of Augmented Reality and Virtual Reality in Education

Augmented Reality (AR) and Virtual Reality (VR) are poised to revolutionize education by offering immersive and interactive learning experiences. This research comprehensively evaluates the educational applications of AR and VR, specifically emphasizing their impact on student motivation, learning outcomes, engagement, and overall learning experiences. The analysis explores how AR and VR can improve student learning, knowledge retention, and skill acquisition by systematically reviewing existing the literature from diverse educational domains, including K-12 education, higher education, STEM education, professional training, and lifelong learning. Additionally, the research investigates the pivotal role of AR and VR in fostering immersive and interactive learning environments, unveiling how these technologies promote active learning, collaboration, and critical thinking through simulations and interactive experiences. The evaluation considers the potential of AR and VR beyond traditional classroom settings in distance education and assesses the feasibility of virtual classrooms, web-based learning environments, and Massive Open Online Courses (MOOCs). A significant aspect of the study involves understanding student attitudes toward AR and VR technologies and their influence on intrinsic motivation, interest, and enthusiasm for the learning material. Based on a thorough analysis of relevant literature, the research aims to provide practical recommendations for educators to effectively incorporate AR and VR into education practices. The recommendations prioritize a pedagogically sound design, educator training, and accessibility consideration to ensure equitable access for all learners. In summary, this extensive research reveals the significant impact of AR and VR on education by understanding the strengths, limitations, and challenges of making informed decisions on utilizing these technologies to create engaging, impactful learning experiences, fostering a generation of technologically proficient and knowledge-driven learners.
Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks

With the recent advancement of multilayer convolutional neural networks (CNN), deep learning has achieved amazing success in many areas, especially in visual content understanding and classification. To improve the performance and energy-efficiency of the computation-demanding CNN, the FPGA-based acceleration emerges as one of the most attractive alternatives. In this paper we design and implement Caffeine, a hardware and software co-designed library to efficiently accelerate the entire CNN on FPGAs. Based on the portable high-level synthesis, Caffeine provides a design automation flow that optimizes and generates FPGA-based AI hardware and runtime software codes. We integrate Caffeine into the industry-standard software deep learning framework.Caffeine achieves a peak performance of 365 GOPS on Xilinx KU060 FPGA and 636 GOPS on Virtex7 690t FPGA, showing up to 7.3x and 43.5x performance and energy gains over Caffe on a 12-core Xeon server, and 1.5x better energy-efficiency over the GPU.
Riding the Waves of Artificial Intelligence in Advancing Accounting and Its Implications for Sustainable Development Goals

Artificial intelligence (AI) is emerging as a disruptive force in many sectors, and using it in accounting isn’t an exception. This conceptual paper explores the role of AI in accounting, for financial reporting, auditing, and financial decision-making and provides accountants an opportunity to improve efficiency, accuracy, and decision support. AI, through data analytics, algorithms, automation, etc. has an important role in the field of accounting with some challenges also. The study also highlights the implications of AI in accounting for achieving several Sustainable Development Goals (SDGs). Firstly, AI-driven automation can restructure financial activities, reducing time and resource consumption, and contributing to SDG 8 (Decent Work and Economic Growth). In addition, by providing real-time data analysis, AI empowers businesses to make sustainable decisions based on real-time data, aligning with SDG 9 (Industry, Innovation, and Infrastructure) and SDG-16 (Peace, Justice, and Strong Institutions) and SDG 17 (Partnerships for the Goals). The paper has implications for policy makers, technology developers, financial institutions and business firms.
Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems

User trust has been identified as a critical issue that is pivotal to the success of autonomous vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such integrated AI-based driving systems, one promising way of building user trust is through the concept of explainable artificial intelligence (XAI) which requires the AI system to provide the user with the explanations behind each decision it makes. Motivated by both the need to enhance user trust and the promise of novel XAI technology in addressing such need, this paper seeks to enhance trustworthiness in autonomous driving systems through the development of explainable Deep Learning (DL) models. First, the paper casts the decision-making process of the AV system not as a classification task (which is the traditional process) but rather as an image-based language generation (image captioning) task. As such, the proposed approach makes driving decisions by first generating textual descriptions of the driving scenarios, which serve as explanations that humans can understand. To this end, a novel multi-modal DL architecture is proposed to jointly model the correlation between an image (driving scenario) and language (descriptions). It adopts a fully Transformer-based structure and therefore has the potential to perform global attention and imitate effectively, the learning processes of human drivers. The results suggest that the proposed model can and does generate legal and meaningful sentences to describe a given driving scenario, and subsequently to correctly generate appropriate driving decisions in autonomous vehicles (AVs). It is also observed that the proposed model significantly outperforms multiple baseline models in terms of generating both explanations and driving actions. From the end user’s perspective, the proposed model can be beneficial in enhancing user trust because it provides the rationale behind an AV’s actions. From the AV developer’s perspective, the explanations from this explainable system could serve as a “debugging” tool to detect potential weaknesses in the existing system and identify specific directions for improvement.
Conflicting evidence fusion using a correlation coefficient-based approach in complex network

Dempster–Shafer evidence theory (D–S theory) can effectively deal with uncertain information and it is one of the effective data fusion methods. However, Dempster’s combination rule of D–S theory often produces counter-intuitive fusion results when the handled body of evidence (BOE) is highly conflicting with each other. Therefore, many new methods have been gradually proposed to optimize BOE to avoid the counter-intuitive fusion results. In this work, inspired by the complex network, a body of evidence is compared to a node, therefore multiple nodes composed of the BOEs constitute a complex network structure, and a correlation coefficient is adopted to measure the degree of correlation between two BOEs. The direct and indirect interaction weights of each node are determined through the direct and indirect interactions among the nodes to reflect their importance in the complex network. After that, the total weight of each BOE is calculated through using the direct and indirect weights. Finally, after modifying the original BOE with weight factor, the final result is obtained after information fusion by using Dempster’s combination rule. This work analyses a practical application case based on the proposed evidential-weighting complex networks in D–S theory. The experiment result shows that the complex network optimization algorithm proposed in this work possesses a good convergence and has significantly improved the counter-intuitive fusion results brought about by the highly conflicting evidence with Dempster’s combination rule.
The enlightening role of explainable artificial intelligence in medical &amp; healthcare domains: A systematic literature review

In domains such as medical and healthcare, the interpretability and explainability of machine learning and artificial intelligence systems are crucial for building trust in their results. Errors caused by these systems, such as incorrect diagnoses or treatments, can have severe and even life-threatening consequences for patients. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a popular area of research, focused on understanding the black-box nature of complex and hard-to-interpret machine learning models. While humans can increase the accuracy of these models through technical expertise, understanding how these models actually function during training can be difficult or even impossible. XAI algorithms such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) can provide explanations for these models, improving trust in their predictions by providing feature importance and increasing confidence in the systems. Many articles have been published that propose solutions to medical problems by using machine learning models alongside XAI algorithms to provide interpretability and explainability. In our study, we identified 454 articles published from 2018-2022 and analyzed 93 of them to explore the use of these techniques in the medical domain.
Open innovation: The missing link between synergetic effect of entrepreneurial orientation and knowledge management over product innovation performance

Scholars stressed over the importance of entrepreneurial orientation and knowledge management for promoting innovation in the enterprises. SMEs, especially in the developing countries are afraid of innovating new products. This research focused particularly on SMEs operating in Jordan which is considered as a heaven for SMEs. The data has been collected with the help of an adopted questionnaires through simple random sampling. The framework has been developed using theoretical lenses of resource-based view, knowledge based view, and contingency theory. A structural equation modelling approach has been applied to test direct and indirect effects. The findings supported all the established relationships except the direct relationship between entrepreneurial orientation and product innovation performance. However, indirect effect of entrepreneurial orientation through open innovation is significant. The findings are highly relevant for the practitioners as they guide that product innovation performance is dependent on open innovation, only entrepreneurial orientation is insufficient.
High-quality color image compression-encryption using chaos and block permutation

This paper proposes a high-quality color image compression-encryption scheme based on chaos and block permutation. In this scheme, the color digital image is first converted and sampled in the YCbCr gamut, and then the coefficients in the sub-blocks are extracted for compression coding after 8×8 post-blocking discrete cosine transformation (DCT) to the frequency domain. Then, an image encryption algorithm using chaos-based block permutation and two-round row-column diffusion is designed for the compressed frequency domain information. In terms of security, a chaos-based block permutation module is designed, which includes the encryption operations of block scrambling, block rotation and inversion, pixel value complement and color component interchange to enhance security. In addition, a mechanism for associating plaintext and intermediate ciphertext is introduced to generate dynamic chaotic sequences to effectively resist various cryptographic attacks. Moreover, the joint compression-encryption reduces the computational complexity of the encrypted object significantly and improves the efficiency. The simulation results show that the joint compression-encryption scheme has the advantages of high compression ratio and high image recovery quality, and has a fairly high security level to resist common cryptographic attacks. Therefore, the compression-encryption scheme proposed in this paper is a preferred and promising method for protecting the privacy of digital images.
Image encoding selection based on Pearson correlation coefficient for time series anomaly detection

Recently, anomaly detection in time-series data has received great attention from researchers due to its importance in problem-solving and predicting future system events. The majority of researchers combine convolutional neural networks (CNN) with image-encoding methods, including Gramian Angular Field (GAF) and Markov Transition Field (MTF). The selection of the right image-encoding method typically involves an iterative evaluation process that requires extensive computational time. This study proposes descriptive statistical analysis and Pearson correlation to find the correlation between statistical features in the encoded data from each image encoding and CNN accuracy. This information is essential for determining the image encoding method without image conversion processes and CNN trials, thus accelerating the computational time. The method tested on the initial dataset showed that the standard-deviation difference has the highest correlation with CNN accuracy (with a Pearson correlation coefficient of r = 0.944), followed by the mean difference (r = 0.705) and the count difference (r = 0.455), also resulting in a 95.35 % reduction in the computation time. The validity of this result was confirmed through the evaluation of several new datasets with different anomaly patterns, making this proposed approach suitable for selecting an appropriate image-encoding method.
Fermion-qudit quantum processors for simulating lattice gauge theories with matter

Simulating the real-time dynamics of lattice gauge theories, underlying the Standard Model of particle physics, is a notoriously difficult problem where quantum simulators can provide a practical advantage over classical approaches. In this work, we present a complete Rydberg-based architecture, co-designed to digitally simulate the dynamics of general gauge theories coupled to matter fields in a hardware-efficient manner. Ref. \cite{Gonzalez_2022} showed how a qudit processor, where non-abelian gauge fields are locally encoded and time-evolved, considerably reduces the required simulation resources compared to standard qubit-based quantum computers. Here we integrate the latter with a recently introduced fermionic quantum processor \cite{Gonzalez_2023}, where fermionic statistics are accounted for at the hardware level, allowing us to construct quantum circuits that preserve the locality of the gauge-matter interactions. We exemplify the flexibility of such a fermion-qudit processor by focusing on two paradigmatic high-energy phenomena. First, we present a resource-efficient protocol to simulate the Abelian-Higgs model, where the dynamics of confinement and string breaking can be investigated. Then, we show how to prepare hadrons made up of fermionic matter constituents bound by non-abelian gauge fields, and show how to extract the corresponding hadronic tensor. In both cases, we estimate the required resources, showing how quantum devices can be used to calculate experimentally-relevant quantities in particle physics.