Reviewerly/exHarmony huggingface dataset:
Galaxy classification based on convolutional neural networks with dropout technique

Galaxy morphology classification is essential for studying the formation and evolution of galaxies. However, previous studies based on Convolutional Neural Network (CNN) mainly focused on the structure of convolutional layers without exploring the designs of fully connected layers. In this regard, this paper trains and compares the performance of CNNs with 4 types of fully connected layers on the Galaxy10 DECaLS dataset. Each type of the fully connected layers contains one dropout layer, and dropout rates including 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% are tested in the experiment to investigate how dropout rates in fully connected layers can affect the overall performance of CNNs. Meanwhile, these models utilize the EfficientNetB0 and DensnNet121 as their feature extraction networks. During the training process, feature-wise standardization, morphological operations, and data argumentation are used for preprocessing. Technics including class weights, exponential learning rate decay, and early stopping are applied to improve model performance. Saliency maps and Grad-CAM are also used to interpret model behaviours. Results show that the architectures of fully connected layers have a significant effect on models overall performance. With the same dropout rate and convolutional layers, models using global average pooling and an additional dense layer outperform others in most cases. The best model obtained an accuracy of 85.23% on test set. Meanwhile, the experimental results on dropout indicate that dropout layer can reduce the effect of the architectures of fully connected layers on overall performance of some CNNs, leading to better performance with less parameters.
A research on different digital signature schemes

In the past years, digital signature development has rapidly with new products combined with blockchain, named distribute networks, and quantum computers, while there plays a vitally important role in file authorization and verification. In combination with various new technologies, digital signatures present a vigorous vitality, and new algorithms are widely used in varieties of scenarios including banking, financial services, and insurance (BFSI), education, E-government, healthcare, and the military. In this case, there is no paper illustrating a summary of those new digital signature applications, which is the aim of this paper working on. This paper indicates the technology details of digital signatures and blockchain. And the paper discusses which digital signature algorithms are used in different fields to give an overview of the relationship between algorithms and scenarios. Furthermore, the paper demonstrates the comparison in the most commonly used digital signature algorithm containing RivestShamirAdleman (RSA) algorithms, Lamport algorithms, Elliptic Curve Digital Signature Algorithm (ECDSA), and Edwards-curve Digital Signature Algorithm (EdDSA) algorithms on their difference in performance.
3D point cloud domain generalization via adversarial training

The purpose of the paper is to tackle the classification problem of 3D point cloud data in domain generalization: how to develop a generalized feature representation for an unseen target domain by utilizing sub-field of numerous seen source domain(s). We present a novel methodology based on both adversarial training to learn a generalized feature representations across subdomains in domain adaptation called 3D-AA. We specifically expand adversarial autoencoders by applying the Maximum Mean Discrepancy (MMD) measure to align the distributions across several subdomains, and then matching the aligned distribution to any given prior distribution via adversarial feature learning. In this manner, the learned 3D feature representation is supposed to be universal to the observed source domains due to the MMD regularization and is expected to generalize well on the target domain due to the addition of the prior distribution. We applied an algorithm to train two different 3D point cloud source domains with our framework. The combination of multiple loss functions on 3D point cloud domain generalization task show that our applied algorithm performs better and learn more generalized features for the target domain than the source-only algorithm which only utilized the MMD measurement.
Spam filter based on naive bayes algorithm

The widespread use of Electronic Mail (E-mail) has led to a significant increase in spam, which has severely impeded the growth and well-being of the Internet. To mitigate this issue, the implementation of email filtering techniques has become necessary, requiring the use of specific technological tools. Presently, the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) algorithms are commonly used in probability statistical classification methods for email filtering. Among these, the NB algorithm is the most classical, with its rich mathematical theory as the basis, high classification efficiency, and straightforward algorithmic approach. However, the algorithm relies on the conditional independence assumption, making the accuracy susceptible to the correlation between attributes. This study focuses on email filtering techniques based on the NB algorithm, conducting experiments to evaluate the classification accuracy and proposing feasible improvements to weaken the independence assumption. The experimental results demonstrated the effectiveness of the employed method.
Towards Improved Heliosphere Sky Map Estimation with Theseus

AbstractThe Interstellar Boundary Explorer (IBEX) satellite has been in orbit since 2008 and detects energy-resolved energetic neutral atoms (ENAs) originating from the heliosphere. Different regions of the heliosphere generate ENAs at different rates. It is of scientific interest to take the data collected by IBEX and estimate spatial maps of heliospheric ENA rates (referred to as sky maps) at higher resolutions than before. These sky maps will subsequently be used to discern between competing theories of heliosphere properties that are not currently possible. The data IBEX collects present challenges to sky map estimation. The two primary challenges are noisy and irregularly spaced data collection and the IBEX instrumentation’s point spread function. In essence, the data collected by IBEX are both noisy and biased for the underlying sky map of inferential interest. In this paper, we present a two-stage sky map estimation procedure called Theseus. In Stage 1, Theseus estimates a blurred sky map from the noisy and irregularly spaced data using an ensemble approach that leverages projection pursuit regression and generalized additive models. In Stage 2, Theseus deblurs the sky map by deconvolving the PSF with the blurred map using regularization. Unblurred sky map uncertainties are computed via bootstrapping. We compare Theseus to a method closely related to the one operationally used today by the IBEX Science Operation Center (ISOC) on both simulated and real data. Theseus outperforms ISOC in nearly every considered metric on simulated data, indicating that Theseus is an improvement over the current state of the art.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
COVID-19 vaccine and test data management – practice report from a university hospital

Abstract Issue The COVID-19 pandemic generated the urgency for creating and implementing new tools, specifically for data collection, sharing. The data governance of healthcare workers (HCW) was layered on top of pre-existing digitalisation issues in health authorities as well as in health facilities. Description of the problem The vaccination act for the German healthcare sector adopted in December 2021, established the compulsory report of HCWs’ COVID-19 vaccination status. The LMU University Hospital management required an overview of the COVID-19 status (illness, isolation, vaccination, infection) of HCWs in order to ensure continuous clinical service. For the timely and accurate report, it was necessary to create a centralised real-time overview of data that were collected and managed by decentralised structures. Results We connected five in-hospital systems into one platform that was being synced three times a day. For each HCW, we created a dataset with three main categories: core personnel data, COVID-19 status and reason for absence. Two user groups were defined: strategic management (1) and operational management (2), where users of group 2 were only given access to their own unit and were required to update and verify data of their team. Overall, the platform contained data of 14,419 HCWs and had 656 users (Ngroup1 = 16, Ngroup2 = 640). Lessons The data merger revealed issues in interoperability and accuracy and thus expedited data standardisation. The decentralised approach facilitated the operational efficiency, digitalisation, service delivery and managerial empowerment at the hospital. The main challenges pertained to data accuracy and to the continuously changing regulatory requirements as well as privacy protection. As all data were previously hosted on different platforms, an exhaustive process of cleansing was needed prior to merging. Each HCW was assigned to an organisational unit with a respective responsible person in order to achieve data quality. Key messages • Dedicated digital human resources solutions in a university hospital pose a specific challenge for the balance between data privacy, availability and interoperability. • To enable a centralised information display, data must be collected where it originates and made available through short paths. Our solution united decentralised collection and centralised management.
Correction: The elementary forms of digital communication

[This corrects the article DOI: 10.1371/journal.pone.0273726.].
18 Exploring the potential of artificial intelligence for automated sentiment

The development of automated sentiment analysis and opinion mining approaches for enhancing the precision and scalability of text analysis has been made possible by the rising integration of artificial intelligence (AI) technology in digital systems. The method of automatically evaluating and extracting sentiment-related data from a text is known as automatic sentiment analysis. Automatically extracting and summarizing opinions from a text is referred to as opinion mining. AI has enormous promise for automated sentiment analysis and opinion mining, which can increase consumer engagement and experience.
Graph Collaborative Filtering and Data Augmentation Strategies in Dual-Target CDR

Current dual-target recommendation methods focus on efficient feature fusion but neglect the inherent noise issues in the domains. However, noise negatively affects the fusion of domains. To tackle this issue, we introduce an improvement and noise reduction strategy named DA-DCDR(Data Augmentation-Dual Target Cross Domain Recommendation), for domain fusion. By refining and reducing noise in each domain’s subgraphs, we not only enhance the accuracy of interaction data but also ensure consistency in data scales. To establish associations between distinct domains, we implement a graph co-training strategy. Key procedures of DA-DCDR include interaction refinement and noise reduction, domain fusion, and correlation expansion. We use graph encoders to acquire user/item embeddings for both domains before domain fusion, followed by enhancement and noise reduction in interactions via top-k sampling and re-prediction. Additionally, we amplify user-user and item-item correlation elements after the domain fusion. Experimental results validate the noteworthy performance enhancement of our proposed strategy in the dual-target recommendation, mitigating the noise effects and boosting the accuracy of the dual-target recommendation system.
Barcode Inbuilt Inter-operable Criminal Justice System: A Game Changer in Forensic Laboratories

Abstract: Advanced techniques are highly required in forensic science laboratories and law enforcement agencies, such as barcoding, QR code, near-field communication (NFC), and radiofrequency identification (RFID), to track and manage forensic evidence. It is requisite to have a unique code system that will identify evidence and convey all the associated information, making capturing, collection, and transfer of data to track assets in real-time more efficient. Barcodes or QR code tag-identifying systems should be used for identification alerts, and their implementation in forensic laboratories will ease communication between the pillars of the criminal justice system. A barcode or QR code-based digital platform can be used to validate the forensic examiners or elected handlers during the examination process and to gain access based on definite functions and duties. The aim of this article was to increase transparency in the forensic case examination, collection, and transfer of data of forensic evidence in order to enhance the satisfaction and trust of the citizens in the forensic, police, and judiciary systems and minimise the chances of intentional and unintended manipulations. To safeguard evidence's integrity and identity, and enhance its accountability, barcodes or QR codes are placed on evidences that are sent to a forensic science laboratory from the scene of the crime. Thus, a scientific approach to modernizing and upgrading the forensic system is strongly recommended by using such technology.
Evaluation Index System of Green Public Open Space Based on Internet of Things and Mental Health

ABSTRACT With the emergence of the IoT era, wireless sensor networks will be more and more widely used. In addition to collecting, transmitting and processing simple data such as humidity, temperature and density of the dome, they can also provide multimedia information services such as video and images. It enables more comprehensive and accurate environmental monitoring. Therefore, MSDs have a huge demand in military, daily, forestry, biomedicine and other fields. The intensive city model has obvious advantages in meeting people's diverse needs and comfortable life. Most obviously, it speeds up the rhythm of life for residents, thereby increasing efficiency and saving time. Starting from this aspect, this paper conducts a research on the evaluation index system of public built on the following areas of open space IoT and mental health. In this paper, the GRNN neural network model is constructed, the mean condition is calculated, the density function can be estimated, the network output, and the schematic diagram of the generalized regression neural network is improved. Using the established system, the index in 2018 is selected as the base year, and after transformation, the standardized values of the past years are formed, which are substituted into the cells to form different matrices. The value of each cell is counted to obtain the subsystem coordination degree, and the global coordination degree is obtained through calculation. The evaluation results of ecological civilization construction and development in 2018 and 2019, 2020 and 2021 were compared. The experimental data shows that compared with 2018, economic development will change from 1 to 2.000, social harmony will change from 1 to 2.480, ecological health will decrease to 0.850, environmental friendliness will decrease to 0.750, and comprehensive evaluation will decrease to 0.513. This shows that while the economy is developing this year, the construction of ecological civilization has been gradually carried out, and good results have been achieved. This reflects the effectiveness of the system. The subject of the evaluation index system of green public open space based on the Internet of Things and mental health has been well completed.
Robust Explainable AI: the Case of Counterfactual Explanations

Counterfactual explanations (CXs) are routinely used to shed light on the decisions of machine learning models; however, CX generation strategies often lack robustness, which may jeopardise their explanatory function. In this talk we will begin by introducing the problem of (lack of) robustness and discuss its implications. We will then present some recent solutions we developed to compute CXs with formal robustness guarantees using tools from discrete optimisation and formal verification.
Museum educational activities in the context of disseminating modern scientific knowledge

The purpose of the study is to analyse the content and focus of the educational activities of museums on the dissemination of modern scientific knowledge and ideas of scientific education. Scientific education is increasingly acquiring interdisciplinary and integrative content as a synthesis of natural, technical, and humanitarian knowledge in formal, non-formal, and informal education, or in conditions of their complementary integration. The authors conducted an online survey of 42 respondents (teachers of Ukrainian secondary schools, teachers of universities, and museum workers) who answered 10 questions of the questionnaire about the organization, drafting, and implementation of museum educational (museum pedagogical) programmes to communicate with pupils and students in the museum environment with the opportunity to express their own attitude to the suggested problems. The survey results proved that teachers of secondary schools and teachers of higher education institutions have a significant interest in the introduction of educational (museum pedagogical) programmes into the environment of national museums with minor visiting reservations, associated with quarantine restrictions during the COVID-19 pandemic. The article analyses the peculiarities of functioning of museum institutions in the system of organizing various types of educational activities with visitors in accordance with the dominant paradigm of education and upbringing. The role and significance of the most important educational paradigms in the system of museum pedagogical activity are outlined. The role of modern museums in promoting and disseminating ideas of scientific knowledge in various forms and methods of work with young people is emphasized.
Preface

The 3rd International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2023) is a leading annual conference. Academics, researchers, and students from across the world are all welcomed to exchange findings and ideas to draw further inspirations in related research fields. CONF-MCEE 2023 encourages communication and collaboration, hoping to provide an international platform for its participants to present, share, and learn the latest advance and development of materials, chemistry and environmental engineering. It’s a great opportunity to deepen your understanding in the related topics. This volume contains the papers presented at the 3rd International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2023), held during March 18-24, 2023. CONF-MCEE 2023 invited professor Ioannis Spanopoulos from University of South Florida, professor Niaz Ahmed from FAST-NUCES, Islamabad, professor Sajjad S. Mofarah from The University of New South Wales, and other professors to provide 5 keynote speeches on related topics of materials, chemistry and environmental engineering. This conference provided the participant good opportunities to exchange ideas and build networks, and it will lead to the further collaborations between both universities and other societies. Three offline workshops have been held at University of South Florida, Tohoku University, and The University of New South Wales before the conference. And an online session was held on the conference date, which is also effective and convenient for academic exchange and communication. Everyone interested in related fields are welcomed to join the online session on our YouTube channel and to give comments and raise questions to the speeches and presentations. We would like to thank the Program Committee members and external reviewers for their hard work in reviewing and selecting papers. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process. Dr. Ioannis Spanopoulos General Chair of Conference Committee List of Committee Members are avaliable in this pdf.
A Novel Texture Mapping Method for Fringe Projection Profilometry

Simultaneous acquisition of texture and depth is an urgent need in applications such as heritage preservation, medical and virtual reality. A phase guided texture mapping method for fringe projection profilometry (FPP) is presented in this paper. For texture acquisition, a color camera is introduced into the monochrome camera-projector FPP system. The projector projects both horizontal and vertical fringe patterns onto the object. Then the monochrome camera and color camera capture the deformed fringe patterns simultaneously. Based on the principle of FPP, the monotonically increasing horizontal and vertical phases can be retrieved from the distorted fringe patterns captured by cameras. Each point on the object has a unique phase pair which includes a horizontal phase value and a vertical phase value. The imaging points to the object point in monochrome camera and color camera have the same phase pair. This helps to find the corresponding point in color camera to the monochrome camera. The new mapped color texture can be calculated by interpolation after the corresponding point found. We studied the theory of this phase guided texture mapping method and present the experiment result. The result shows this method is robust and accurate.
A Flexible Calibration Method for Fringe Projection Profilometry

The fringe projection profilometry (FPP) is an optical method which reconstruction 3D object by using structured light. The Calibration is inevitable in FPP. The calibration procedure is to establish a connection between height and light field information. The existing calibration methods usually need precise z stages and/or interpolation on markers and are complex and time-cost. We present a flexible calibration algorithm for fringe projection profilometry. It avoids these by using ray-phase model and designing a two-step calibration procedure. The ray-phase equations formulate the relation of 3D coordinates and phase. During the calibration procedure, an auxiliary camera is introduced to construct a stereo vision system with measurement system. Firstly, the stereo vision system is calibrated by using traditional method. Then the coordinates and absolute phases, which are both obtained by measuring the calibration object with the stereo vision system, are applied to fit ray-phase equations. Both theoretical analysis and experimental results are provided. In the experiment, standard deviation of the plane is 0.0422 mm. They show that our calibration algorithm is flexible, feasible and accurate.