Reviewerly/exHarmony huggingface dataset:
CAN: Context-assisted full Attention Network for brain tissue segmentation

Brain tissue segmentation is of great value in diagnosing brain disorders. Three-dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic Resonance Imaging (MRI) suffer from high time complexity and low segmentation accuracy, respectively. To address these two issues, we propose a Context-assisted full Attention Network (CAN) for brain MRI segmentation by integrating 2D and 3D data of MRI. Different from the fully symmetric structure U-Net, the CAN takes the current 2D slice, its 3D contextual skull slices and 3D contextual brain slices as the input, which are further encoded by the DenseNet and decoded by our constructed full attention network. We have validated the effectiveness of the CAN on our collected dataset PWML and two public datasets dHCP2017 and MALC2012. Our code is available at https://github.com/nwuAI/CAN.
Expert-Level Immunofixation Electrophoresis Image Recognition based on Explainable and Generalizable Deep Learning

Immunofixation electrophoresis (IFE) is important for diagnosis of plasma cell disorders (PCDs). Manual analysis of IFE images is time-consuming and potentially subjective. An artificial intelligence (AI) system for automatic and accurate IFE image recognition is desirable.In total, 12 703 expert-annotated IFE images (9182 from a new IFE imaging system and 3521 from an old one) were used to develop and test an AI system that was an ensemble of 3 deep neural networks. The model takes an IFE image as input and predicts the presence of 8 basic patterns (IgA-, IgA-, IgG-, IgG-, IgM-, IgM-, light chain and ) and their combinations. Score-based class activation maps (Score-CAMs) were used for visual explanation of the models prediction.The AI model achieved an average accuracy, sensitivity, and specificity of 99.82, 93.17, and 99.93, respectively, for detection of the 8 basic patterns, which outperformed 4 junior experts with 1 years experience and was comparable to a senior expert with 5 years experience. The Score-CAMs gave a reasonable visual explanation of the prediction by highlighting the target aligned regions in the bands and indicating potentially unreliable predictions. When trained with only the new system images, the models performance was still higher than junior experts on both the new and old IFE systems, with average accuracy of 99.91 and 99.81, respectively.Our AI system achieved human-level performance in automatic recognition of IFE images, with high explainability and generalizability. It has the potential to improve the efficiency and reliability of diagnosis of PCDs.
Demonstration of universal time-reversal for qubit processes

In quantum mechanics, the unitary nature of time evolution makes it intrinsically reversible, given control over the system in question. Remarkably, there have been several recent demonstrations of protocols for reverting unknown unitaries in scenarios where even the interactions with the target system are unknown. These protocols are limited by their probabilistic nature, raising the fundamental question of whether time-reversal could be performed deterministically. Here we show that quantum physics indeed allows for this by exploiting the non-commuting nature of quantum operators, and demonstrate a recursive protocol for two-level quantum systems with an arbitrarily high probability of success. Using a photonic platform, we achieve an average rewinding fidelity of over 95%. Our protocol, requiring no knowledge of the quantum process to be rewound, is optimal in its running time, and brings quantum rewinding into a regime of practical relevance.
An adaptive mutual K-nearest neighbors clustering algorithm based on maximizing mutual information

Clustering based on Mutual K-nearest Neighbors (CMNN) is a classical method of grouping data into different clusters. However, it has two well-known limitations: (1) the clustering results are very much dependent on the parameter k; (2) CMNN assumes that noise points correspond to clusters of small sizes according to the Mutual K-nearest Neighbors (MKNN) criterion, but some data points in small size clusters are wrongly identified as noises. To address these two issues, we propose an adaptive improved CMNN algorithm (AVCMNN), which consists of two parts: (1) improved CMNN algorithm (abbreviated as VCMNN) and (2) adaptive VCMNN algorithm (abbreviated as AVCMNN). Specifically, the first part is VCMNN algorithm, we first reassign the data points in some small-size clusters by a novel voting strategy because some of them are wrongly identified as noise points, and the clustering results are improved. Then, the second part is AVCMNN, we use maximizing mutual information to construct an objective function to optimize the parameters of the proposed method and finally obtain the better parameters values and clustering results. We conduct extensive experiments on twenty datasets, including six synthetic datasets, ten UCI datasets, and four image datasets. The experimental results show that VCMNN and AVCMNN outperforms three classical algorithms (i.e., CMNN, DPC, and DBSCAN) and six state-of-the-art (SOTA) clustering algorithms in most cases.
Constructing a high-performance self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies for gold exploration targeting

In an area where mineral deposits have been discovered, both anomaly detection algorithms and supervised classification algorithms can be adopted to detect mineralization-related geochemical anomalies for mineral exploration targeting. However, anomaly detection algorithms themselves cannot use known mineral deposits as the supervisors to improve the performance of geochemical anomaly detection models. Supervised classification algorithms can not properly deal with the extreme class-imbalance of geochemical exploration data in the establishment of classification models. Therefore, neither anomaly detection models nor supervised classification models perform well in the detection of mineralization-related geochemical anomalies. In order to obtain a high-performance mineral exploration targeting model, the self-training algorithm was adopted to construct a self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies in the Chengde area in Hebei Province (China). The self-training model was compared with the support vector classification model, logistic regression model and five dictionary learning models in terms of area under the curve (AUC) and lift index. The AUC value of the self-training model (0.89) is much higher than those of the two supervised classification models (0.81–0.82) and five anomaly detection models (0.79–0.81). The lift index of the self-training model (11.87) is also much higher than those of the two supervised classification models (2.77–4.04) and five anomaly detection models (2.64–3.48). Therefore, the self-training model performs much better than the two supervised classification models and five anomaly detection models in the detection of gold mineralization-related geochemical anomalies. The gold mineralization-related geochemical anomalies detected by the self-training model highly conform to the metallogenic and geological features in the study area. Therefore, it is a prospective method to establish a self-training model to detect mineralization-related geochemical anomalies in an area where mineral deposits have been discovered.
Cubature Kalman filter with closed-loop covariance feedback control for integrated INS/GNSS navigation

Cubature Kalman Filter (CKF) offers a promising solution to handle the data fusion of integrated nonlinear INS/GNSS (Inertial Navigation System/Global Navigation Satellite System) navigation. However, its accuracy is degraded by inaccurate kinematic noise statistics which originate from disturbances of system dynamics. This paper develops a method of closed-loop feedback covariance control to address the above problem of CKF. In this method, the posterior state and its covariance are fed back to the filtering process to constitute a closed-loop structure for CKF covariance propagation. Subsequently, based on the maximum likelihood principle, a control scheme of the prior state covariance is established by using the feedback state and covariance within an estimation window and further adopting a proportional coefficient to amplify the feedback terms in recent time steps for the full use of new information to reflect actual system characteristics. Since it does not directly use kinematic noise covariance, the proposed method can effectively avoid the adverse impact of inaccurate kinematic noise statistics on filtering solutions. Further, it can also guarantee the prior state covariance to be positive semi-definite without involving extra measures. The efficacy of the proposed method is validated by simulations and experiments for integrated INS/GNSS navigation.
Automated Approach to IaC Code Inspection Using Python-Based DevSecOps Tool

One of main benefits enabled by DevOps ideology is to automatize activities and operations related to development, testing, integration and deployment of software, to fulfill the needs of relevant organization’s goals. On the other side, quality of code, security, together with compliance according to given standards represent highly relevant considerations. In this paper, we present an open-source Python-based tool with web-based graphical interface which enables automation of static code analysis and checks when it comes to Infrastructure as Code (IaC) scripts. The proposed tool is evaluated in several scenarios when it comes to terraform scripts.
Caribbean Metaverse Development: A Literature Review Perspective

The Caribbean’s metaverse evolution accelerated due to the Covid-19 pandemic. This paper focuses on the metaverse, XR, and NFT and emphasises the Caribbean’s contribution to the virtual environment. A bibliometric analysis of metaverse-themed research identified the rapid increase in publications in 2021 and 2022 and that titles with XR (AR, VR or MR) occurred three times more than blockchain (including NFT). An evolving dataset was created based on a continuous scoping literature review of Industry 4.0 and its enabling technologies. This enables the creation of a new definition of the metaverse, understanding the UX benefits of XR and its applications' areas of foci, highlighting investment in XR-based projects, and illustrating the Caribbean-themed NFT and XR projects. This dataset revealed that UX benefits are linked to XR element features that are relevant, contextual, customised, hands-free and intuitive. It also revealed that XR applications have areas of foci that can enable machine control or data interface, designing and testing, remote support, education, customer engagement, remote collaboration or entertainment and escapism. Analysis of 54 XR papers revealed that the most popular area of focus was education (including training, learning and understanding). An evaluation of global investments in XR development showed funding ranged from USD 70K to USD 100M, and there needs to be focused financial support for Caribbean projects. This justifies continued research into factors influencing funding and encouraging Caribbean XR development. In addition, this research promotes regionally developed XR projects and NFTs. The paper's originality is the reductionist definition of the metaverse: a space designed for users by users, which can satisfy whomever, whatever, however, wherever and whenever. It manifests the user's extended reality, facilitated through XR technologies that enable Industry 4.0 (I4.0). As such, the metaverse can be considered the practical implementation of I4.0.
MedBERT: A Pre-trained Language Model for Biomedical Named Entity Recognition

This paper introduces MedBERT, a new pre-trained transformer-based model for biomedical named entity recognition. MedBERT is trained with 57.46M tokens collected from biomedical-related data sources, i.e. datasets acquired from N2C2, BioNLP, CRAFT challenges, and biomedical-related articles crawled from Wikipedia. We validate the effectiveness of MedBERT by comparing it with four publicly available pre-trained models on ten biomedical datasets from BioNLP and CRAFT shared tasks. Our experimental results show that models fine-tuned on MedBERT achieve state-of-the-art performance in nine datasets that predict Protein, Gene, Chemical, Cellular/Component, Gene Ontology, and Taxonomy entities. Specifically, the model achieved an average of 84.04% F1-micro score on ten test sets from BioNLP and CRAFT challenges with an improvement of 3.7% and 7.83% as compared to models that were fine-tuned on BioBERT and Bio_ClinicalBERT, respectively.
Humans and/or robots? Tourists’ preferences towards the humans–robots mix in the service delivery system

This paper investigates tourists’ preferences toward the humans-robots ratio in the service delivery systems of tourism and hospitality companies and the factors that shape them. The sample includes 1537 respondents from nearly 100 countries. The findings show that a higher preferred share of robots is positively associated with the perceived emotional skills of robots, their perceived usefulness in the tourism/hospitality context, perceived robotic service expectations, attitudes towards robots in general, and the male gender. On the other side, it is negatively associated with the perceived disadvantages of robots compared to human servers and the household size of respondents.
Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.
Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review

The risk of cyberattacks against businesses has risen considerably, with Business Email Compromise (BEC) schemes taking the lead as one of the most common phishing attack methods. The daily evolution of this assault mechanism’s attack methods has shown a very high level of proficiency against organisations. Since the majority of BEC emails lack a payloader, they have become challenging for organisations to identify or detect using typical spam filtering and static feature extraction techniques. Hence, an efficient and effective BEC phishing detection approach is required to provide an effective solution to various organisations to protect against such attacks. This paper provides a systematic review and examination of the state of the art of BEC phishing detection techniques to provide a detailed understanding of the topic to allow researchers to identify the main principles of BEC phishing detection, the common Machine Learning (ML) algorithms used, the features used to detect BEC phishing, and the common datasets used. Based on the selected search strategy, 38 articles (of 950 articles) were chosen for closer examination. Out of these articles, the contributions of the selected articles were discussed and summarised to highlight their contributions as well as their limitations. In addition, the features of BEC phishing used for detection were provided, as well as the ML algorithms and datasets that were used in BEC phishing detection models were discussed. In the end, open issues and future research directions of BEC phishing detection based on ML were discussed.
On the effect of the thermal cross-talk in a photonic feed-forward neural network based on silicon microresonators

Local heating is widely used to trim or tune photonic components in integrated optics. Typically, it is achieved through the power dissipation of metal microwires driven by a current and placed nearby the photonic component. Then, via the thermo-optic effect, both the amplitude and the phase of the complex optical field propagating in the component can be controlled. In the last decade, optical integrated circuits with a cascade of more than 60 thermo-optical phase shifters were demonstrated for quantum simulators or optical neural networks. In this work, we demonstrate a simple two layers feed-forward neural network based on cascaded of thermally controlled Mach-Zehnder interferometers and microring resonators. We show that the dynamics of a high quality factor microresonator integrated into a Silicon On Insulator (SOI) platform is strongly affected by the current flowing in metal heaters where these last generate both local as well as global heating on the integrated photonic circuit. Interestingly, microheaters, even when they are at distances of a few millimetres from the optical component, influence all the microresonators and the Mach-Zehnder interferometers in the photonic circuit. We model the heat flux they generate and modify accordingly the non-linear equations of a system formed by a microresonator coupled to a bus waveguide. Furthermore, we show experimentally that the use of microheaters can be a limiting factor for the feed-forward neural network where three microresonators are used as non-linear nodes. Here, the information encoding, as well as the signal processing, occurs within the photonic circuit via metal heaters. Specifically, the network reproduces a given non-linear surjective function based on a domain of at most two inputs and a co-domain of just one output. As a result, its training aims to determine the values of the currents to apply to the heaters in the hidden layers, which allows replicating a certain shape. We demonstrate how the network exploits mainly the heat flow generated by the information encoding to reproduce a target avoiding the use of all the hidden layer heaters. This work shows that in large thermally actuated integrated photonic circuit, the thermal cross talk is an issue.
“AR The Gods of Olympus”: Design and Pilot Evaluation of an Augmented Reality Educational Game for Greek Mythology

Teaching and learning theoretical subjects, such as History, although important, is considered by many students to be non-appealing. Alternative teaching approaches include the use of educational games and augmented reality (AR) applications, or more recently, AR educational games. Such games are considered to increase students’ interest for the subject and lead to better learning outcomes. However, studies about the use of AR educational games in the classroom are sparse and further research is necessary. In this article, we present an AR-enhanced educational game for teaching History (Greek Mythology) to 3rd grade Primary school students in Greece. The game, called “AR The Gods of Olympus” consists of three mini games: an AR game with the gods/goddesses of Olympus using narration; a memory game with cards depicting the gods and their symbols; and a quiz game. In order to study the effectiveness of the game and students’ experience and perceptions on it, a study was carried out with primary school students that used the game in classroom. The study utilized a pre-/post-test design, a brief questionnaire based on the MEEGA+ model for evaluating educational games, and observation of students during game playing. Students’ performance was improved after playing the game but the difference was not statistically significant, while the game was positively perceived by students. Especially the AR mini game raised students’ interest and as the students themselves stated helped them “learn while playing”.
Multi-Job Intelligent Scheduling With Cross-Device Federated Learning

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this article, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).
Drug Recommendation from Diagnosis Codes: Classification vs. Collaborative Filtering Approaches

Over time, large amounts of clinical data have accumulated in electronic health records (EHRs), making it difficult for healthcare professionals to navigate and make patient-centered decisions. This underscores the need for healthcare recommendation systems that help medical professionals make faster and more accurate decisions. This study addresses drug recommendation systems that generate an appropriate list of drugs that match patients' diagnoses. Currently, recommendations are manually prepared by physicians, but this is difficult for patients with multiple comorbidities. We explored approaches to drug recommendations based on elderly patients with diabetes, hypertension, and cardiovascular disease who visited primary-care clinics and often had multiple conditions. We examined both collaborative filtering approaches and traditional machine-learning classifiers. The hybrid model between the two yielded a recall at 5 of 76.61%, a precision at 5 of 46.20%, a macro-averaged area under the curve of 74.52%, and an average physician agreement of 47.50%. Although collaborative filtering is widely used in recommendation systems, our results showed that it consistently underperformed traditional classification. Collaborative filtering was sensitive to class imbalances and favored the more popular classes. This study highlighted challenges that need to be addressed when developing recommendation systems in EHRs.