Reviewerly/exHarmony huggingface dataset:
Development of Students’ Skills through Gamification and Serious Games: An Exploratory Study

Currently, technological development is making its way into education through the increasing integration of new technologies into teaching. In this context, gamification emerges as one of the ways to incorporate the pedagogical process into the digital world, creating the need to encourage both the digital skills of educators and students. The objective of this work is to shed light on the growing use of technology-based teaching methods and encourage their integration into educational practice from the teacher’s point of view. This work specifically analyses the European Framework for Digital Competence of Educators, relates the different digital competences defined to a series of localized educational solutions, and proposes a direct relationship between different gamification tools and serious games. Based on the review carried out, this study highlights a profound change in the educational process that involves digitization and the use of new technologies to optimize learning, as well as the training needs of teaching staff and the development of educational solutions that take into account the different digital competences.
Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior

This paper presents a novel path planning heuristic for multi-UAV pipe inspection missions inspired by the booby bird’s foraging behavior. The heuristic enables each UAV to find an optimal path that minimizes the detection time of defects in pipe networks while avoiding collisions with obstacles and other UAVs. The proposed method is compared with four existing path planning algorithms adapted for multi-UAV scenarios: ant colony optimization (ACO), particle swarm optimization (PSO), opportunistic coordination, and random schemes. The results show that the booby heuristic outperforms the other algorithms in terms of mean detection time and computational efficiency under different settings of defect complexity and number of UAVs.
Application of Logistic Regression and Weights of Evidence Methods for Mapping Volcanic-Type Uranium Prospectivity

Pucheng district is a part of the Wuyi Mountain polymetallic metallogenic belt, which is constituted by Archean-Proterozoic metamorphic basements and Mesozoic volcanic-sedimentary covers. Uranium deposits are formed as volcanic-hosted and structural controls. In this study, the hybrid data-driven methods of logistic regression (LR) and weights of evidence (WofE) were applied for the mineral potential mapping of uranium in the Pucheng district. Evidential layers such as volcanic stratum, structure, igneous rock, alteration and radioactive anomaly were used in the mineral prospectivity analyses. The results show that the data-driven methods can not only measure the relative importance of each type of geological feature in uranium controls but also delineate prospective grounds for uranium exploration. The receiver operating characteristics (ROC) curve and under the ROC curve (AUC) were applied to measure the performance of the prospectivity models. The data-driven models are highly capable of mapping uranium prospectivity because AUC is close to 1. The results show that more than 90% of the known uranium deposits occur in regions with high probability. LR performs a little better than WofE in this area. The prospectivity mapping confirmed that there is significant potential for uranium mineralization for further exploration.
CCTrans: Improving Medical Image Segmentation with Contoured Convolutional Transformer Network

Medical images contain complex information, and the automated analysis of medical images can greatly assist doctors in clinical decision making. Therefore, the automatic segmentation of medical images has become a hot research topic in recent years. In this study, a novel architecture called a contoured convolutional transformer (CCTrans) network is proposed to solve the segmentation problem. A dual convolutional transformer block and a contoured detection module are designed, which integrate local and global contexts to establish reliable relational connections. Multi-scale features are effectively utilized to enhance semantic feature understanding. The dice similarity coefficient (DSC) is employed to evaluate experimental performance. Two public datasets with two different modalities are chosen as the experimental datasets. Our proposed method achieved an average DSC of 83.97% on a synapse dataset (abdominal multi-organ CT) and 92.15% on an ACDC dataset (cardiac MRI). Especially for the segmentation of small and complex organs, our proposed model achieves better segmentation results than other advanced approaches. Our experiments demonstrate the effectiveness and robustness of the novel method and its potential for real-world applications. The proposed CCTrans network offers a universal solution with which to achieve precise medical image segmentation.
A Manhattan distance based hybrid recommendation system

Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions[20]. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches[1]. This paper describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance-based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.
M2FINet: Modality-specific and Modality-shared Features Interaction Network for RGB-IR Person Re-Identification

Learning rich and discriminative person-related modality-shared feature representations to distinguish the same person in different modalities is significant for RGB-Infrared Person Re-IDentification (RGB-IR ReID). However, most existing models often directly extract modality-shared features from those modality-specific features without considering their interactions. This will result in the ineffective exploration of person-related modality-shared features. To address such a problem, a novel Modality-specific and Modality-shared Features Interaction Network (M2FINet) model is proposed for RGB-IR ReID in this paper. Especially, in the proposed M2FINet model, a Cross-level Feature Guidance and Injection (CFGI) module is carefully designed to establish and exploit the interactions between the middle-level modality-specific features and the high-level modality-shared features. Specifically, the proposed CFGI module mainly consists of two streams, a shared-to-specific feature guidance (H2P) stream and a specific-to-shared feature injection (P2H) stream. Among that, the H2P stream aims to take high-level modality-shared features as the prior information to guide the re-exploration of more rich and discriminative modality-shared semantic information from such middle-level modality-specific features. The P2H stream aims to enhance the representation ability and discriminability of high-level modality-shared features by introducing more modality-shared detail information from the middle-level modality-specific features. On top of that, a simple but effective feature aggregation module, i.e., Focusing on Person (FOP), is further designed in our proposed model to reinforce such discriminative modality-shared features within those person-related regions via a multi-pooling feature aggregation manner. Extensive experiments on two public benchmarks, i.e, SYSU-MM01 and RegDB, show that our proposed model consistently improves the accuracy of RGB-IR ReID. Without bells and whistles, it achieves Rank-1/mAP by 74.73%/68.96% on the large-scale SYSU-MM01 dataset.
Optimal deep neural networks by maximization of the approximation power

We propose an optimal architecture for deep neural networks of given size. The optimal architecture obtains from maximizing the lower bound of the maximum number of linear regions approximated by a deep neural network with a ReLu activation function. The accuracy of the approximation function relies on the neural network structure characterized by the number, dependence and hierarchy between the nodes within and across layers. We show how the accuracy of the approximation improves as we optimally choose the width and depth of the network. A Monte-Carlo simulation exercise illustrates the outperformance of the optimized architecture against cross-validation methods and gridsearch for linear and nonlinear prediction models. The application of this methodology to the Boston Housing dataset confirms empirically the outperformance of our method against state-of the-art machine learning models.
Modality Translation and Fusion for event-based semantic segmentation

Semantic segmentation is an important part of scene understanding in autonomous systems. Accurate segmentation results help the autonomous system better understand the surrounding environment, which improve the task success rate and enhance the system reliability. However, due to the hardware limitations of frame-based cameras, semantic segmentation based on RGB images under low-light conditions is still a difficult problem. The event camera, an emerging bio-inspired vision sensor, tries to fill these gaps with its high dynamic range. Most previous works resort to cross-modality knowledge distillation from a pretrained image-based teacher network to train an event-based network for semantic segmentation. However, a direct knowledge distillation is inadequate because of the different input modalities in the two networks. Utilizing image features to supervise event features cannot make full use of the knowledge in the teacher network. Thus, we propose a Modality Translation and Fusion (MTF) framework to distill diverse cross-modality knowledge. Specifically, we first develop a Modality Translation (MT) module to convert events into image modality. With different input modalities, two feature extractors are built to learn diverse and complementary knowledge from the teacher network. Then, to better utilize the knowledge, we propose a Residual-based Coordinate Attention Fusion (RCAF) module to fuse the multi-scale features from different modalities. Finally, extensive experiments show that our MTF is superior to state-of-the-art (SOTA) approaches.
Qubit Quasi-Probability Coherence Induced by a Nonlinear Coherent Cavity Filled With a Kerr-Like Medium Under Dissipation Effect

The work considers a qubit interacting off-resonantly a nonlinear Kerr-like quantum-harmonic-oscillator cavity field through nonlinear intensity-dependent and one-photon interactions. The analytical solution for the master equation is obtained when the qubit starts with an excited pure state while the harmonic-oscillator field starts with a coherent state. The dynamics of the phase space Husimi-distribution and its Wehrl-Husimi entropy entanglement/mixedness is explored under the effects of the atom-field detuning, Kerr-like nonlinearity as well as atomic spontaneous-emission dissipation. For resonant case, the Wehrl-Husimi entropy qubit-oscillator entanglement and atomic mixedness are generated (due to the unitary nonlinear intensity-dependent evolution) with a regular oscillatory behavior. For off-resonant case, the quantum coherence is generated partially with a high-frequency irregular oscillatory behavior. The Kerr-like nonlinearity and the atomic spontaneous dissipation lead to enhancing the generated atomic mixedness Wehrl-Husimi entropy enhances and stabilizing the atomic state in a maximally mixed state. The phase space Husimi-distribution information dynamics of the corresponding the generated atomic mixed states confirms the vital link between the formed interference Husimi-distributions and the generated atomic Wehrl-Husimi entropy mixedness. It is found that the dynamics of the Husimi-distribution information and its Wehrl-Husimi entropy is highly sensitive to the qubit-cavity detuning, Kerr-like nonlinearity as well as the dissipation.
Research on the Impact of Digital Transformation of Commercial Banks on Profitability

Under the background of digital finance, this paper uses Stata to empirically study the impact of digital transformation on the profitability of commercial banks. This study examines how digital transformation affects the profitability of commercial banks by developing a fixed effect regression model using data from 16 Chinese listed commercial banks from 2012 to 2021 as research samples. Furthermore, the heterogeneity analysis is conducted to investigate the difference of the impact of digital transformation of different types of commercial banks on profitability. The research results show that digital transformation significantly increases the profitability of commercial banks. The digital transformation of large state-owned commercial banks cannot significantly improve their profitability, while the digital transformation of joint-stock commercial banks and urban commercial banks can significantly improve their profitability. Finally, based on the research results, suggestions are made for commercial banks and financial regulators.
The Impacts and Challenges of Artificial Intelligence Translation Tool on Translation Professionals

Machine translation, especially translation based on neural network technology has made a major breakthrough and is increasingly accepted and widely used. The development of artificial intelligence (AI) translation has had a definite impact on translation jobs. People, even professional translators, are relying on AI translation. But There is no research on whether machine translation software is superior to professional translators in translating various types of documents.. In this study, we design an experiment to determine the advantages and disadvantages between AI translations and human translations. The result shows the impact of the development of AI on the translation industry. To achieve better translation results and output highquality translations in the era of rapid development of AI, it will contribute to Human-AI partnerships.
A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://www.optim-app.com/projects/cfa , https://www.mathworks.com/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa .
Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons

An increasing number of threat actors takes advantage of information hiding techniques to prevent detection or to drop payloads containing attack routines. With the ubiquitous diffusion of mobile applications, high-resolution icons should be considered a very attractive carrier for cloaking malicious information via steganographic mechanisms. Despite machine learning approaches proven to be effective to detect hidden payloads, the mobile scenario could challenge their deployment in realistic use cases, for instance due to scalability constraints. Therefore, this paper introduces an approach based on federated learning able to prevent hazards characterizing production-quality scenarios, including different privacy regulations and lack of comprehensive datasets. Numerical results indicate that our approach achieves performances similar to those of centralized solutions.
Diktyo: Network-Aware Scheduling in Container-Based Clouds

Containers have revolutionized application deployment and life-cycle management in current cloud platforms.Applications have evolved from single monoliths to complex graphs of loosely-coupled microservices.However, the efficient allocation of microservice-based applications is challenging due to their complex inter-dependencies.Further, recent applications are becoming even more delay-sensitive, demanding lower latency between dependent microservices.Scheduling policies in popular container orchestration platforms mainly aim to increase the resource efficiency of the infrastructure, insufficient for latencysensitive applications.Application domains such as the Internet of Things and multi-tier web services would benefit from networkaware policies that consider network latency and bandwidth in the scheduling process.Previous works have studied networkaware scheduling via theoretical formulations or heuristic-based methods evaluated via simulations or small testbeds, making their full applicability in popular platforms difficult.This paper proposes a novel network-aware framework for the popular Kubernetes (K8s) platform named Diktyo that determines the placement of dependent microservices in long-running applications focused on reducing the application's end-to-end latency and guaranteeing bandwidth reservations.Simulations show that Diktyo can significantly reduce the network latency for various applications across different infrastructure topologies compared to default K8s scheduling plugins.Also, experiments in a K8s cluster with microservice benchmark applications show that Diktyo can increase database throughput by 22% and reduce application response time by 45%.
Fine-Tuning Transformer Models for Adverse Drug Event Identification and Extraction in Biomedical Corpora: A Comparative Study

Adverse Drug Events (ADEs) are potentially fatal problems that patients can deal with only if they have a solid awareness of them. With the available amount of unstructured textual data from biomedical literature, electronic records, and social media (e.g., tweets), early detection of unfavorable reactions and sharing them with biomedical experts, pharma companies, and healthcare professionals is a necessity, as this can prevent morbidity and save many lives. The Biomedical Named Entity Recognition (BioNER) task can be considered the initial step toward resolving this issue. In this paper, we present an empirical evaluation experiment by fine-tuning pretrained language models for detecting biomedical entities (e.g., drug-names and symptoms). We fine-tuned five transformer models: BERT (Bidirectional Encoder Representations from Transformers), SpanBERT, BioBERT, BlueBERT, and SCIBERT, on two well-known biomedical datasets, CADEC and ADE-corpus. The evaluation results demonstrate that BioBERT which was pretrained on both general and domain-specific (biomedical domain) corpora outperformed all other models on both datasets and reached 90.3% and 68,73% on the F1-score in the ADE and CADEC corpora, respectively.
The Uprising of Blockchain Technology in the Energy Market Industry

There is an increasing demand for decarbonisation due to environmental and climate concerns, which has led to research efforts being focused on improving the rate of renewable energy integration. Energy markets are promising mechanisms to promote this noble cause by encouraging distributed renewable energy generation whilst providing auxiliary energy management services. Despite these advantages, numerous challenges hinder its widescale adoption. Facilitating energy exchanges through blockchain technology can overcome some of these challenges. The concept is relatively new, with visible research gaps. This paper promotes awareness regarding the applicability of blockchain technology in the energy market industry through a comprehensive literature re- view and a conceptual peer-to-peer energy exchange simulation. Various research opportunities have also been provided.
An investigation of financial contagion between cryptocurrency and equity markets: Evidence from developed and emerging markets

The present study conducts a dynamic conditional cross-correlation and time–frequency correlation analyses between cryptocurrency and equity markets in both advanced and emerging economies. The purpose of the study is twofold. First, the study investigates the presence of the pure (narrow) form of financial contagion between cryptocurrency and stock markets in both advanced and emerging economies, during the black swan event of the COVID-19 crisis. Second, the study examines the hedging and safe-haven properties of cryptocurrencies against equity markets, before and during periods of financial upheaval triggered by the COVID-19 pandemic. Two econometric models are used: (1) the dynamic conditional correlation (DCC) GARCH and (2) the wavelet analysis models. Using the DCC GARCH model, the study found the evidence of high conditional correlations between cryptocurrency and equity markets. The high conditional correlation was mostly detected in periods of financial turmoil corresponding to the first quarter and the second quarter of 2020. The increase in conditional correlation during periods of financial upheaval (compared to a tranquil period) indicates the presence of the pure form of financial contagion. The wavelet cross-correlation analysis showed the evidence of positive cross-correlation between the Bitcoin and the equity markets during period of financial turmoil. The cross-correlation was identified in both short and long (coarse) scales. In short scales, the equity markets lead the cryptocurrency market, while the cryptocurrency market leads equity markets in coarse scales. The findings of the present study revealed that the degree of interdependence between cryptocurrency and equity markets has substantially increased during the COVID-19 period, and this has negated the safe-haven and hedging benefits of cryptocurrencies over equity markets.