Reviewerly/exHarmony huggingface dataset:
TUTORING – PEDAGOGICAL INNOVATION IN PROFESSIONAL AND PEDAGOGICAL TRAINING OF TEACHERS IN HIGHER EDUCATION INSTITUTIONS

The article is devoted to the theoretical substantiation of the expediency of introducing tutoring technology as a type of pedagogical innovation in the professional and pedagogical training of a teacher at a higher education institution. It was found that professional and pedagogical training is an important normative condition within the framework of the pedagogical master's degree in higher education in the preparation of future teachers of the institution of higher education. Due to the considered psychological and pedagogical concepts, which are related to the concept of "individual education", we managed to outline the technology of individualization of education. This made it possible to generalize that professional-pedagogical activity can be implemented in a higher education institution within the framework of a pedagogical master's degree as one of the types of pedagogical innovation based on an individualized approach to learning - tutoring technology. Tutoring is able to fully implement the professional and pedagogical training of a teacher of a higher education institution based on acmeological, humanistic, personal-activity, context-competence approaches. At the same time, ensure flexibility, variability and practical orientation of professional and pedagogical training at various stages of educational and scientific activity.
LCDnet: a lightweight crowd density estimation model for real-time video surveillance

Abstract Automatic crowd counting using density estimation has gained significant attention in computer vision research. As a result, a large number of crowd counting and density estimation models using convolution neural networks (CNN) have been published in the last few years. These models have achieved good accuracy over benchmark datasets. However, attempts to improve the accuracy often lead to higher complexity in these models. In real-time video surveillance applications using drones with limited computing resources, deep models incur intolerable higher inference delay. In this paper, we propose (i) a Lightweight Crowd Density estimation model (LCDnet) for real-time video surveillance, and (ii) an improved training method using curriculum learning (CL). LCDnet is trained using CL and evaluated over two benchmark datasets i.e., DroneRGBT and CARPK. Results are compared with existing crowd models. Our evaluation shows that the LCDnet achieves a reasonably good accuracy while significantly reducing the inference time and memory requirement and thus can be deployed over edge devices with very limited computing resources.
Effective implementation of nonadiabatic geometric quantum gates of cat-state qubits using an auxiliary qutrit

Abstract We propose an effective protocol for the implementation of nonadiabatic geometric quantum gates of cat-state qubits in Kerr-nonlinear resonators driven by two-photon squeezing drives. Coupling the Kerr-nonlinear resonators with an auxiliary qutrit with proper coupling strengths, the selective transition of the auxiliary qutrit is realized. The selective transition can be exploited in the implementation of a set of useful quantum gates, including the phase gates, the NOT gates, the controlled-phase gates, the controlled NOT gates, and the Toffoli gates. Numerical simulations show the implementations of different types of gates are robust against systematic errors, random noise, and decoherence. Therefore, the protocol may be helpful for robust and scalable quantum computation based on cat-state qubits.
Tutoring Postgraduate Students with an AI-Based Chatbot

Tutoring and counselling are essential support services universities provide to their students. With the ever-increasing number of the students, providing these functions has become harder and harder. Therefore, universities have started to use chat robots or chatbots to automate their guidance and counselling services. This paper discusses the suitability of the chatbots or virtual advisers in personal study planning and course selection in higher educational institutions. The empiric part of the study reports the observations of the experiment conducted in a Finnish university of applied sciences. In the study postgraduate students of engineering used a tailor-made chatbot while creating their personal study plans. The findings of the study indicated that chatbots could to some extent improve student counselling and the main advantages were scalability and unlimited service hours. However, students did not see that artificial intelligence could fully remove the need for human counselling. The main reported shortcomings of the chatbot were the minor significance of the individuality, lack of inspiring effect, and general negative attitude towards automated services.
Fermionic quantum processing with programmable neutral atom arrays

Simulating the properties of many-body fermionic systems is an outstanding computational challenge relevant to material science, quantum chemistry, and particle physics. Although qubit-based quantum computers can potentially tackle this problem more efficiently than classical devices, encoding non-local fermionic statistics introduces an overhead in the required resources, limiting their applicability on near-term architectures. In this work, we present a fermionic quantum processor, where fermionic models are locally encoded in a fermionic register and simulated in a hardware-efficient manner using fermionic gates. We consider in particular fermionic atoms in programmable tweezer arrays and develop different protocols to implement non-local tunneling gates, guaranteeing Fermi statistics at the hardware level. We use this gate set, together with Rydberg-mediated interaction gates, to find efficient circuit decompositions for digital and variational quantum simulation algorithms, illustrated here for molecular energy estimation. Finally, we consider a combined fermion-qubit architecture, where both the motional and internal degrees of freedom of the atoms are harnessed to efficiently implement quantum phase estimation, as well as to simulate lattice gauge theory dynamics.
Preprocessing for Keypoint-Based Sign Language Translation without Glosses

While machine translation for spoken language has advanced significantly, research on sign language translation (SLT) for deaf individuals remains limited. Obtaining annotations, such as gloss, can be expensive and time-consuming. To address these challenges, we propose a new sign language video-processing method for SLT without gloss annotations. Our approach leverages the signer's skeleton points to identify their movements and help build a robust model resilient to background noise. We also introduce a keypoint normalization process that preserves the signer's movements while accounting for variations in body length. Furthermore, we propose a stochastic frame selection technique to prioritize frames to minimize video information loss. Based on the attention-based model, our approach demonstrates effectiveness through quantitative experiments on various metrics using German and Korean sign language datasets without glosses.
A causality-weighted approach for prioritizing mining 4.0 strategies integrating reliability-based fuzzy cognitive map and hybrid decision-making methods: A case study of Nigerian Mining Sector

Nigeria's economy depends solely on crude oil with little attention to the agricultural and solid mineral sectors which were the major sources of revenue prior to the 1970s “oil boom”. However, some improvements were recently recorded from the solid mineral sector as the sector contributed meaningfully to the GDP in the last three years. The contribution of this sector is still extremely low as compared to the oil sector. Hence, there is need to explore the recent technology in improving the solid mineral contribution to GDP. This study therefore explores the application of industrial 4.0 for selecting the best strategy in the Nigerian mining sector using the extended combined compromise solution (COCOSO) technique with the fuzzy cognitive map (FCM). The fuzzy COCOSO is structured by using causal-and-effect weights. Notably, the FCM and fuzzy-COCOSO are implemented in a reliable environment as the Z-number concept is used for overcoming uncertainty of system. Ten copies of questionnaires comprising nine mining 4.0 strategies (alternatives) and 15 criteria were provided by a ten-member team of experts. The main goals in the decision system were minimising implementation costs (G1), maximising financial incomes from smart mining strategies (G2), maximising government financial support to use smart technologies (G3), minimising hazardous events (G4), and maximising safety in operations (G5), which are considered in the ZFCM topology. In the final step of research, the effectiveness and robustness of the nine proposed mining 4.0 policies were investigated by using a parametric sensitivity analysis. Based on the presented hybrid model, it is concluded that the third policy (A3, internet of things) is the best option for intelligent mining in the Nigerian mining sector. It was confirmed by an expert team that obtained results were valid and that the factors and conditions in the study area were worth evaluating and validating.
A new phishing-website detection framework using ensemble classification and clustering

Phishing websites are characterized by distinguished visual, address, domain, and embedded features, which identify and defend such threats. Yet, phishing website detection is challenged by overlapping these features with legitimate websites’ features. As the inter-class variance between legitimate and phishing websites becomes low, commonly utilized machine learning algorithms suffer from low performance in overlapping feature cases. Alternatively, ensemble learning that combines multiple predictions intending to address low inter-class variations in the classified data improves the performance in such cases. Ensemble learning utilizes multiple classifiers of similar or different types with multiple deviations of the training data. This paper develops a framework based on random forest ensemble techniques. The limitations of the random forest are the inability to capture the high correlation between features and their join dependency on the label. The random forest is combined with k-means clustering to capture the feature correlation. The framework is evaluated for phishing detection with a dataset of 5000 samples. The results showed the proposed framework over-performed the random forest classifier, all other ensemble classifiers, and the conventional classification algorithms. The proposed framework achieved an accuracy of 98.64%, precision of 0.986, recall of 0.987, and F-measure of 0.986.
Bots against corruption: Exploring the benefits and limitations of AI-based anti-corruption technology

Countries have been developing and deploying anti-corruption tools based on artificial intelligence with hopes of them having positive capabilities. Yet, we still lack empirical analyses of these automated systems designed to identify and curb corruption. Hence, this article explores novel data on 31 bottom-up and top-down initiatives in Brazil, presented as a case study. Methodologically, it uses a qualitative analysis and draws on secondary data and interviews to assess the most common features, usages and constraints of these tools. Data collected are scrutinised under a new conceptual framework that considers how these tools operate, who created them for what purpose, who uses and monitors these tools, what types of corruption they are targeting, and what their tangible outcomes are. Findings suggest that in Brazil, AI-based anti-corruption technology has been tailored by tech-savvy civil servants working for law enforcement agencies and by concerned citizens with tech skills to take over the key tasks of mining and crosschecking large datasets, aiming to monitor, identify, report and predict risks and flag suspicions related to clear-cut unlawful cases. The target is corruption in key governmental functions, mainly public spending. While most of the governmental tools still lack transparency, bottom-up initiatives struggle to expand their scope due to high dependence on and limited access to open data. Because this new technology is seen as supporting human action, a low level of concern related to biased codes has been observed.
Medical image encryption based on RNG with an autonomous piecewise damping Josephson junction jerk oscillator embedded in FPGA

Abstract This paper explore the dynamics, Field Programmable Gate Array (FPGA) validation of an autonomous piecewise damping Josephson junction jerk oscillator (APDJJJO) and uses it to protect medical images based on random number generator (RNG). APDJJJO is derived from a piecewise damping Josephson junction (JJ) model and has either no equilibrium points or two unstable equilibrium points. One-scroll chaotic hidden attractor, one-scroll sovereign complex attractor, periodic and one-scroll complex self-driven attractors coexisting, and bistable limit cycles are found in APDJJJO during the investigation based on numerical simulations. Additionally, the FPGA validation of the APDJJJO shows similar complex characteristics to those obtained during the investigation via numerical simulations. Lastly, the chaotic characteristics depicted by the APDJJJO are used to design a RNG for the encryption of medical images. The generated random bits are validated successfully by standard statistical tool set by the National Institute of Standards and Technology (NIST-800-22). Encryption algorithm is developed to secure a medical image by exploring the unpredicted bits generated. The safety and performances analysis are done to prove the robustness and efficiency of the image encryption algorithm.
Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.
Long distance multiplexed quantum teleportation from a telecom photon to a solid-state qubit

Quantum teleportation is an essential capability for quantum networks, allowing the transmission of quantum bits (qubits) without a direct exchange of quantum information. Its implementation between distant parties requires teleportation of the quantum information to matter qubits that store it for long enough to allow users to perform further processing. Here we demonstrate long distance quantum teleportation from a photonic qubit at telecom wavelength to a matter qubit, stored as a collective excitation in a solid-state quantum memory. Our system encompasses an active feed-forward scheme, implementing a conditional phase shift on the qubit retrieved from the memory, as required by the protocol. Moreover, our approach is time-multiplexed, allowing for an increase in the teleportation rate, and is directly compatible with the deployed telecommunication networks, two key features for its scalability and practical implementation, that will play a pivotal role in the development of long-distance quantum communication.
Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology

Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.
Exact solution for the quantum and private capacities of bosonic dephasing channels

The capacities of noisy quantum channels capture the ultimate rates of information transmission across quantum communication lines, and the quantum capacity plays a key role in determining the overhead of fault-tolerant quantum computation platforms. In the case of bosonic systems, central to many applications, no closed formulas for these capacities were known for bosonic dephasing channels, a key class of non-Gaussian channels modelling, e.g., noise affecting superconducting circuits or fiber-optic communication channels. Here we provide the first exact calculation of the quantum, private, two-way assisted quantum, and secret-key agreement capacities of all bosonic dephasing channels. We prove that that they are equal to the relative entropy of the distribution underlying the channel to the uniform distribution. Our result solves a problem that has been open for over a decade, having been posed originally by [Jiang & Chen, Quantum and Nonlinear Optics 244, 2010].
Central Bank Digital Currency and Financial Inclusion

In this paper, we develop a model incorporating the impact of financial inclusion to study the implications of introducing a retail central bank digital currency (CBDC).CBDCs in developing countries (unlike in advanced countries) have the potential to bank large unbanked populations and boost financial inclusion which can increase overall lending and reduce bank disintermediation risks.Our model captures two key channels.First, CBDC issuance can increase bank deposits from the previously unbanked by incentivizing the opening of bank accounts for access to CBDC wallets (offsetting potential flows from deposits to CBDCs among those already banked).Second, data from CBDC usage allows for the building of credit to reduce credit-risk information asymmetry in lending.We find that CBDC can increase overall lending if (1) bank deposit liquidity risk is low, (2) the size and relative wealth of the previously unbanked population is large, and (3) CBDC is valuable to households as a means of payment or for credit-building.CBDC can still be optimal for household welfare even when overall lending decreases as households benefit from the value of using CBDC for payments, CBDC provides an alternative "safe" savings vehicle, and CBDC generates greater surplus in lending by reducing credit-risk information asymmetry.Most countries are considering a "two-tier" CBDC model, where central banks issue CBDC to commercial banks which in turn distribute them to consumers.If non-bank payment system providers can distribute CBDC, fewer funds will flow into deposit accounts from the unbanked because a bank account is no longer needed to access CBDC.If CBDC data is shareable with banks, those without bank accounts can still build credit and access lower interest rate loans.This design is optimal for welfare if the gains from greater access to CBDC outweigh the contraction in lending.
Quantum Many-Body Theory from a Solution of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math> -Representability Problem

Here we present a many-body theory based on a solution of the $N$-representability problem in which the ground-state two-particle reduced density matrix (2-RDM) is determined directly without the many-particle wave function. We derive an equation that re-expresses physical constraints on higher-order RDMs to generate direct constraints on the 2-RDM, which are required for its derivation from an $N$-particle density matrix, known as $N$-representability conditions. The approach produces a complete hierarchy of 2-RDM constraints that do not depend explicitly upon the higher RDMs or the wave function. By using the two-particle part of a unitary decomposition of higher-order constraint matrices, we can solve the energy minimization by semidefinite programming in a form where the low-rank structure of these matrices can be potentially exploited. We illustrate by computing the ground-state electronic energy and properties of the ${\mathrm{H}}_{8}$ ring.