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Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts. [ABSTRACT FROM AUTHOR]

Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields. [ABSTRACT FROM AUTHOR]

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model. [ABSTRACT FROM AUTHOR]

Objective: to analyse the artistic bindings of the eighteenth-century prints stored in the Historical-artistic Binding Database of the Royal Library, in order to locate the endpapers made with decorated papers, and to classify and describe them according to the proposals of the specialist literature. Methodology: The records from the database were retrieved using the "date" field, and the information is extracted from the "endpapers" and "notes" fields. The reproductions of the endpapers were analysed, identified and described following the classification model of decorated papers inspired by the works of Professor Carpallo Bautista, consisting of five techniques and their variants. Finally, a comparative analysis was carried out. Results: Among the 382 eighteenth-century prints with artistic bindings, ten were located and analysed that include endpapers decorated with xylographic plates, 135 with marbled or mottled papers (with designs corresponding to seven variants), four with paste-glued papers, three with dotted papers, 18 with gold and embossed papers, and four of other kinds such as silver or percaline paper. [ABSTRACT FROM AUTHOR]

Der Beitrag gibt einen Überblick zum Stand der Besucherforschung in Museen ausgehend vom physischen hin zum digitalen Publikum. Dabei werden einige Problemfelder der aktuellen Forschung aufgezeigt. Im Mittelpunkt stehen das digitale Museumspublikum und sein Informationssuchverhalten. Ausgehend von verschiedenen, sehr heterogenen Studien wird versucht allgemeingültige Erkenntnisse zum Suchverhalten des Online-Publikums zu gewinnen. Die daraus entwickelten Kategorien sollen Hinweise für die praktische Anwendung liefern, um Suchvorgänge zu unterstützen und erfolgreicher zu machen. Darauf aufbauend wird ein vereinfachtes, praxisorientiertes Kategorisierungsschema für Online-Besucher vorgeschlagen und auf Alternativen zur feldbasierten Suche eingegangen. The paper provides an overview of the state of the art of visitor research in museums, both traditional and virtual visitors followed by highlighting some problematic issues of current audience research. The paper focuses on digital museum audiences and how they search for information. Based on an analysis of a number of quite heterogeneous studies, both a generalising synopsis of search behaviour and a simplified model of online visitor classification are developed. In addition, alternatives to fielded searching are discussed. [ABSTRACT FROM AUTHOR]

Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article. Hybrid spectral similarity algorithms are tested on forensic analysis of paper data. We compared the classification capabilities of various hybrid spectral similarity algorithms on hyperspectral data of 40 different paper samples. The overall accuracy (OA), kappa K̂, Z‐score of kappa (ZK̂), and the 95% confidence interval of kappa (CI(K̂)) are used for comparison. The SID‐SAM and SID‐SCA produced an overall accuracy of 88% and 87%, respectively, which is highest among the hybrid spectral similarity measures tested. [ABSTRACT FROM AUTHOR]

The eighteenth century books from the collection of the old digital library of the Library University Seville are analyzed to locate the parts made with decorated papers, in order to classify and describe them according to the proposals of specialized literature. For this, the records included in the repository of the digital collection are recovered, the digital reproductions of the books are visualized, the parts with decorated papers are identified and described following the decorated papers classification model, previously elaborated, based on the research of Professor Carpallo Bautista. The model is made up of 6 techniques and their variants. Of the more than 6.700 records from the 18th century in the collection of the digital ancient collection, 511 include decorated papers in their bindings. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods. [ABSTRACT FROM AUTHOR]

To conclude, we believe that this special issue accurately portrays the scientific features of the CLADAG community nowadays and supports the CLADAG mission of facilitating the exchange of ideas in Classification and Data Analysis. This special issue of Statistical Analysis and Data Mining contains a selection of the papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG), scheduled for September 9-11, 2021 in Florence, Italy. The articles cover a broad range of data analysis topics: gender gap analysis, income clustering, structural equation modeling, multivariate nonparametric methods, and classifier selection. [Extracted from the article]

Seizures are the most common neurological emergency in the neonatal period and in contrast to those in infancy and childhood, are often provoked seizures with an acute cause and may be electrographic‐only. Hence, neonatal seizures may not fit easily into classification schemes for seizures and epilepsies primarily developed for older children and adults. A Neonatal Seizures Task Force was established by the International League Against Epilepsy (ILAE) to develop a modification of the 2017 ILAE Classification of Seizures and Epilepsies, relevant to neonates. The neonatal classification framework emphasizes the role of electroencephalography (EEG) in the diagnosis of seizures in the neonate and includes a classification of seizure types relevant to this age group. The seizure type is determined by the predominant clinical feature. Many neonatal seizures are electrographic‐only with no evident clinical features; therefore, these are included in the proposed classification. Clinical events without an EEG correlate are not included. Because seizures in the neonatal period have been shown to have a focal onset, a division into focal and generalized is unnecessary. Seizures can have a motor (automatisms, clonic, epileptic spasms, myoclonic, tonic), non‐motor (autonomic, behavior arrest), or sequential presentation. The classification allows the user to choose the level of detail when classifying seizures in this age group. [ABSTRACT FROM AUTHOR]

The classification of private archives determines a planned process required to be done in accordance with the archiving method and techniques according to the type and contents of the documents they hold. Determination of the archival value of the document or material that adds the quality of being private to an archive sets the fundamental aim of the materials or documents within the private archive should be determined. However they have been acquired, for the later similar processes in the classification of such archives to be understood the terminology, applied method and techniques should be clearly explained and recorded. Especially the person archives are examples where more delicate work should be done in this regard. In these samples, where generally document based work is needed, person, person's family, kith and kin, work life, socio-economic status and similar properties can be surfaced via documents and materials. Via terminology and applied methods used accurately, these private archives can be presented to the interest of the public as archives with local, national and international value. The person archive of which the classification and regulation procedures are tried to be explained in the stud, was taken for supervision in 2016 and classification works have continued until November 2018. The archive classified according to 11 main records / material type(s), consists of documents/materials produced and taken in between 1899-1990. Each main type is organized according to the proper techniques to reveal the access and relations within itself. In the classification and arrangement work, the document type, method, equipment and legal terminology are defined based on literature considering the understanding of the process and that it can be example for the similar later works. In this regard, the basic aim of the work is determined as the explanation of the terminology and archiving methods needed to be used in classification and arrangement process of a private archive with examples. classification. For the fulfillment of this aim the types, production goals, relations of the [ABSTRACT FROM AUTHOR]

Oil immersed paper insulation condition is a crucial aspect of power transformer's life condition diagnostics. The measurements testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. In order to generate a reliable model, more studies related to machine learning implementation to power transformer assessment need to be done. In this article, the objective of the study is to develop reliable new approach in transformer oil-immersed paper insulation condition assessment based on SVM-classifier model using its oil measurements. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm employed for developing classification model is Support Vector Machine (SVM). The model had been trained and tested using different datasets. Several different models have been created and the best has been chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 transformers which Furan data is not available. The classification results were combined, reviewed, and compared to conventional assessment methods and standards. The comparation confirmed that the model developed has the ability to do classification of current oil-paper condition for the transformer population observed, based on Dissolved Gasses and Dielectric Characteristics. [ABSTRACT FROM AUTHOR]

Given the need in Scientometrics to get beyond merely classifying scientific production based on the classification of the journals in which it is published, there have been many attempts to classify papers directly. Little has been done, however, to check how reliable the results are. In this work, a collection of publications was generated which we call an Author's Assignation Collection (AAC) comprising 13449 papers referenced in the Scopus database and classified by their research guarantor with fractional weighting in terms of Scopus's own ASJC scheme. The methodological approach taken is described, and the collection's representativeness is evaluated and compared with the journal-based classification. There stand out both the great number of papers assigned by their research guarantors to more than one category (at times with even the same weight) and how frequently authors assigned categories which were not assigned to the journals in which their paper was published. [ABSTRACT FROM AUTHOR]

With the rapid development of the decorative papers industry on a worldwide scale, the aesthetic assessment of decorative papers has evolved as one of the major fields for industrial production. This study was performed to investigate the ability of visible spectroscopy and NIR spectroscopy coupled with the soft independent modeling of class analogy (SIMCA) to reflect the surface characteristics of decorative paper and to classify decorative papers with different visual characteristics. The results showed that visible spectroscopy has a higher relationship with the surface characteristics of decorative papers than the NIR data during PCA analysis due to larger variations. Additionally, when using visible spectroscopy (400 to 780 nm), the classification accuracy reached 94% to 100%, a more accurate result than could be achieved based on color data. In the results of the NIR spectroscopy (780 to 2500 nm), the classification accuracy decreased to the range 1% to 56%, except for a value of 95% for the samples that were grained with a slightly dark color, and a greater number of samples were assigned to more than one class. There were significant differences in the performance of the models built with visible spectroscopy and NIR spectroscopy, so it can be concluded that visible spectroscopy coupled with SIMCA is more useful to classify the different types of decorative papers than NIR spectroscopy. [ABSTRACT FROM AUTHOR]

The digital trunking system has the characteristics that the calling mode is mainly group calling and the communication mode is mostly half duplex. For the problem that the traditional user classification method couldn't meet the classification requirements of digital cluster users, this paper proposed a digital cluster user classification method based on improved RFM model. Firstly, it introduced the average speech duration attribute to establish the recency vitality speak (RVS) model. Then, it used the analytic hierarchy process to determine the weight of each parameter in the model. Finally, it used the K-means + + clustering algorithm to classify digital cluster users. The simulation result shows that, by using the user classification method proposed in this paper, the accuracy of digital cluster user classification can reach more than 87. 9% . [ABSTRACT FROM AUTHOR]

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. [ABSTRACT FROM AUTHOR]

Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model's categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification, clustering, or prediction), the models' input and output parameters, the performance metrics, the optimal model, and the model's benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries. [ABSTRACT FROM AUTHOR]

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model's performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. [ABSTRACT FROM AUTHOR]

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines. [ABSTRACT FROM AUTHOR]

This paper explores some challenges of archaeological interpretation and classification through an enclosure at Inchnadamph in Sutherland, northwestern Scotland, a site that has proven difficult to interpret. Despite a small number of archaeological interventions, including topographic and geophysical survey and excavation, the site remains enigmatic. We discuss the different interpretations suggested for the site in turn, concluding that the enclosure does not fit readily into existing classification schemes. This raises issues surrounding the use of classification systems, prior assumptions, and the need for critical thinking in interpretation. These are universal issues, applicable beyond the Scottish example chosen, and this paper highlights concerns and difficulties encountered by all who deal with the classification of sites and monuments. Ultimately, this challenges some of our preconceptions and sheds light on the limits of our knowledge, as well as the limits of our classification systems. [ABSTRACT FROM AUTHOR]

A stochastic network simulation is verified when its distribution of outputs is aligned with the ground truth, while tolerating deviations due to variability in real-world measurements and the randomness of a stochastic simulation. However, comparing distributions may yield false positives, as erroneous simulations may have the expected distribution yet present aberrations in low-level patterns. For instance, the number of sick individuals may present the right trend over time, but the wrong individuals were infected. We previously proposed an approach that transforms simulation traces into images verified by machine learning algorithms that account for low-level patterns. We demonstrated the viability of this approach when many simulation traces are compared with a large ground truth data set. However, ground truth data are often limited. For example, a publication may include few images of their simulation as illustrations; hence, teams that independently re-implement the model can only compare low-level patterns with few cases. In this paper, we examine whether our approach can be utilized with very small data sets (e.g., 5–10 images), as provided in publications. Depending on the network simulation model (e.g., rumor spread, cascading failure, and disease spread), we show that results obtained with little data can even surpass results obtained with moderate amounts of data at the cost of variability. Although a good accuracy is obtained in detecting several forms of errors, this paper is only a first step in the use of this technique for verification; hence, future works should assess the applicability of our approach to other types of network simulations. [ABSTRACT FROM AUTHOR]

Segmentation of whole slide histological images through the classification of tissue types of small fragments is an extremely relevant task in digital pathology, necessary for the development of methods for automatic analysis of whole slide histological images. The extremely large resolution of such images also makes the task of increasing image resolution relevant, which allows storing images at a reduced resolution and increasing it if necessary. Annotating whole slide images by histologists is complex and time-consuming, so it is important to make the most efficient use of the available data, both labeled and unlabeled. In this paper we propose a novel neural network method to simultaneously solve the problems of super-resolution of histological images from ×20 optical magnification to ×40 and classifying image fragments into tissue types at ×20 magnification. The use of a single encoder as well as the proposed neural network training scheme allows to achieve better results on both tasks compared to existing approaches. The PATH-DT-MSU WSS2v2 dataset presented for the first time in this paper was used for training and testing the method. On the test sample, an accuracy value of 0.971 and a balanced accuracy value of 0.916 were achieved in the classification task on 5 tissue types, for the super-resolution task, values of PSNR = 32.26 and SSIM = 0.89 were achieved. The source code of the proposed method is available at: https://github.com/Kukty/WSI_SR_CL. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments. [ABSTRACT FROM AUTHOR]

Introduction: Public health ethics addresses moral dilemmas arising from balancing individual healthcare needs with societal interests. Ethical considerations in public health during pandemics and disasters aim to reduce mortality rates and minimize social injustice through fair principles. Objective: This paper analyzes public health ethics and ethical values in allocating resources during mass casualty incidents. The intersection of public health ethics, applied bioethics, and ethics of social consequences (through non-utilitarian consequentialism) guides addressing serious public health challenges in catastrophic scenarios. The application of the given interaction is significant for professional medical ethics. Methodology: The paper employs inductive, deductive, and normative methods of bioethics and the methodology of ethics of social consequences. Conclusion: The paradigmatic disparity between the bioethics of pandemics and disaster bioethics lies in the fluid application of bioethical principles and the accentuation of utilitarian demands depending on the severity and scale of mass casualty incidents. Applied bioethics in crisis situations respects the approaches of public health ethics and attempts to increase positive social outcomes. The application of (scarce) resource allocation criteria and triage of patients is derived from ethical decisions beneficial to public health and lege artis approaches of medical bioethics. The paper presents professional and ethical criteria for medically inappropriate treatment within the framework of patient triage; we approach crisis ethics from the perspective of maximization of benefit. Age is not an exclusion criterion of acute healthcare provision in crisis situations. Ethics of social consequences as a form of non-utilitarian consequentialism allowing for social consequences bridges public health ethics and applied bioethics. [ABSTRACT FROM AUTHOR]

Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application. [ABSTRACT FROM AUTHOR]

This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation. [ABSTRACT FROM AUTHOR]

Persian poetry has profoundly affected all periods of Persian literature and the literature of other countries as well. It is a fundamental vehicle for expressing Persian culture and political opinion. This paper presents a corpus of Persian literary text mainly focusing on poetry, covering the ninth to twenty-first century annotated for century and style, with additional partial annotation of rhetorical figures. Our resource is the largest and the most diverse corpus available in Persian literary text, with a particularly broad temporal scope. This allows us to conduct several computational experiments to analyze poetic styles, authors and time periods, as well as context shifts over time, for which we rely both on supervised models and on Persian poetry-specific heuristics. The corpus, the tools, and experiments described in this paper can be used not only for digital humanities studies of Persian literature but also for processing Persian texts in general, as well as in other broader cross-linguistic applications. [ABSTRACT FROM AUTHOR]

Confronted with the fast social development, the speed of garbage generation has increased. Therefore, from every aspect, especially staying green and healthy, it is urgent and vital to ensure accurate and stable garbage classification. In the field of garbage classification, household garbage has a large volume and the classification process still requires manual intervention. Besides, most of the current studies focus on industrial waste and medical waste, while the existing studies on household garbage are not effective enough. Furthermore, these studies are mostly trained and validated in self-built datasets, making the experimental results insufficiently representative and severely limiting the development of research. Aiming to facilitate the study of household garbage classification, this paper proposes a simple yet effective network model (FConvNet) to improve classification performance. In this paper, an effective convolution block with fused convolution is designed to enhance the potential correlation of the features in the spatial dimension, and then a lightweight symmetric structure is constructed to infer more information about the feature representation. The experiments on public datasets have justified the effectiveness of FConvNet with accuracy rates of 95%, 95% and 97%, achieving better performance on public datasets compared to baselines. Additionally, an online system based on the WeChat mini program framework is designed to provide convenient household garbage classification services and enable the collection of uploaded images to perform model optimizations. [ABSTRACT FROM AUTHOR]

Development of a hydrogen economy as part of the response to the challenges especially of climate change and energy security will depend upon the solution not only of technical problems, but also of legal and regulatory problems. Whereas these are most obviously related to health and safety, this paper focuses on the fact that support for hydrogen is increasingly contingent upon the method of its production and specifically upon the level of greenhouse gas emissions involved. The paper examines emerging arrangements in Australia and the EU as examples of likely future key hydrogen exporters and importers, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a method for developing a malware ontology structure by detecting malware instances on Twitter. The ontology represents a semi-automatic classifier fed by the data extracted from tweets. In particular, the automatic part of the presented methodology relies on a pattern-based approach to detect trigger expressions leading to new information about malware, whilst the manual one covers the evaluation of the results by domain-experts, who also validate the reliability of the semantic relationships within the ontology framework. We present preliminary results on the application of our methodology to tweets extracted from MalwareBazaar database showing how the documents’ collection analysis, through Natural Language Processing (NLP) tasks, can support the knowledge retrieval and documents’ classification procedures for building early warning system of detected malware. Results obtained from this research paper within the time framework of 2023 are referred to the previous version of the current social network X. [ABSTRACT FROM AUTHOR]

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments. [ABSTRACT FROM AUTHOR]

• Artificial intelligence can facilitate inclusive approach to heritage planning. • This paper reveals the cultural significance attached to the heritage property. • Artificial intelligence can contribute to value-based heritage planning. Experts have always played an important role in heritage planning, practice, and theory. There is a wealth of literature published every year regarding heritage and its cultural significance. Experts also contribute to heritage planning and developing policy documents. Still, literature is rarely used as a source of primary research to systematically reveal and compare experts' opinions on the cultural significance of built heritage. Analyzing them as a whole is costly and time-consuming, especially on built heritage, when much has been written about. While the automation of methods has proven to mitigate such restrictions in other fields, as digital humanities, their application in heritage planning, practice, and theory is still scarce. Hence, this paper aims to investigate the potentials of AI models (e.g., multi label text classification) in analyzing scientific documents, revealing the cultural significance of built heritage, values and attributes. This was done to better understand the similarities and differences between the experts' opinions. Yazd, Iran, is taken a case study, with a particular focus on windcatchers, a key attribute conveying cultural significance, of outstanding universal value, due to its inscription on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the application of AI in heritage planning; 2) methodology of data collection and data analysis related to coding values and attributes of windcatchers, addressed in relevant documents; 3) preliminary findings on the experts' opinions over values and attributes of windcatchers. Results contribute to the scientific discussion, revealing the cultural significance of windcatchers of Yazd from experts' point of view. Besides, the potential of AI for heritage planning is revealed in terms of (de)coding and measuring the cultural significance of built heritage from the available documents, showing the opinions of experts with various backgrounds. This model can be applied to other key attributes in Yazd and other case studies and scales to support heritage planning, practice, and theory. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explaining their decisions remains an open investigation. Yet, the explainability of DCNN models has become essential for healthcare systems to accept and trust the models. This paper presents a novel framework for explaining DCNN classification decisions of lesions in ultrasound images using the saliency maps linking the DCNN decisions to known cancer characteristics in the medical domain. The proposed framework consists of three main phases. First, DCNN models for classification in ultrasound images are built. Next, selected methods for visualization are applied to obtain saliency maps on the input images of the DCNN models. In the final phase, the visualization outputs and domain-known cancer characteristics are mapped. The paper then demonstrates the use of the framework for breast lesion classification from ultrasound images. We first follow the transfer learning approach and build two DCNN models. We then analyze the visualization outputs of the trained DCNN models using the EGrad-CAM and Ablation-CAM methods. We map the DCNN model decisions of benign and malignant lesions through the visualization outputs to the characteristics such as echogenicity, calcification, shape, and margin. A retrospective dataset of 1298 US images collected from different hospitals is used to evaluate the effectiveness of the framework. The test results show that these characteristics contribute differently to the benign and malignant lesions' decisions. Our study provides the foundation for other researchers to explain the DCNN classification decisions of other cancer types. [ABSTRACT FROM AUTHOR]

Network traffic classification is crucial for identifying network applications and defending against network threats. Traditional traffic classification approaches struggle to extract structural features and suffer from poor interpretability of feature representations. The high symmetry between network traffic classification and its interpretable feature representation is vital for network traffic analysis. To address these issues, this paper proposes a traffic classification and feature representation model named the attention mechanism autoencoder (AMAE). The AMAE model extracts the global spatial structural features of network traffic through attention mechanisms and employs an autoencoder to extract local structural features and perform dimensionality reduction. This process maps different network traffic features into one-dimensional coordinate systems in the form of spectra, termed FlowSpectrum. The spectra of different network traffic represent different intervals in the coordinate system. This paper tests the interpretability and classification performance of network traffic features of the AMAE model using the ISCX-VPN2016 dataset. Experimental results demonstrate that by analyzing the overall distribution of attention weights and local weight values of network traffic, the model effectively explains the differences in the spectral representation intervals of different types of network traffic. Furthermore, our approach achieves the highest classification accuracy of up to 100% for non-VPN-encrypted traffic and 99.69% for VPN-encrypted traffic, surpassing existing traffic classification schemes. [ABSTRACT FROM AUTHOR]

Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system's classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process. [ABSTRACT FROM AUTHOR]

Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80% in using only 10% of the graphs, and iteratively built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25% of the graphs. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves A C C r o b u s t scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate. [ABSTRACT FROM AUTHOR]

Aims and objectives: To assess and compare the quality of paper-based and electronic- based health records. The comparison examined three criteria: content, documentation process and structure. Background: Nursing documentation is a significant indicator of the quality of patient care delivery. It can be either paper-based or organised within the system known as the electronic health records. Nursing documentation must be completed at the highest standards, to ensure the safety and quality of healthcare services. However, the evidence is not clear on which one of the two forms of documentation (paper-based versus electronic health records is more qualified. Methods: A retrospective, descriptive, comparative design was used to address the study’s purposes. A convenient number of patients’ records, from two public hospitals, were audited using the Cat-ch-Ing audit instrument. The sample size consisted of 434 records for both paper-based health records and electronic health records from medical and surgical wards. Results: Electronic health records were better than paper-based health records in terms of process and structure. In terms of quantity and quality content, paper-based records were better than electronic health records. The study affirmed the poor quality of nursing documentation and lack of nurses’ knowledge and skills in the nursing process and its application in both paper-based and electronic-based systems. Conclusion: Both forms of documentation revealed drawbacks in terms of content, process and structure. This study provided important information, which can guide policymakers and administrators in identifying effective strategies aimed at enhancing the quality of nursing documentation. Relevance to clinical practice: Policies and actions to ensure quality nursing documentation at the national level should focus on improving nursing knowledge, competencies, practice in nursing process, enhancing the work environment and nursing workload, as well as strengthening the capacity building of nursing practice to improve the quality of nursing care and patients’ outcomes. [ABSTRACT FROM AUTHOR]

This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

Paper aging and degradation are growing concerns for those who are responsible for the conservation of documents, archives, and libraries. In this study, the paper aging was investigated using laser-induced fluorescence spectroscopy (LIFS), where the fluorescence properties of 47 paper samples with different ages were explored. The paper exhibits fluorescence in the blue-green spectral region with two peaks at about 448 nm and 480 nm under the excitation of 405 nm laser. Both fluorescence peaks changed in absolute intensities and thus the ratio of peak intensities was also influenced with the increasing ages. By applying principal component analysis (PCA) and k-means clustering algorithm, all 47 paper samples were classified into nine groups based on the differences in paper age. Then the first-derivative fluorescence spectral curves were proposed to figure out the relationship between the spectral characteristic and the paper age, and two quantitative models were established based on the changes of first-derivative spectral peak at 443 nm, where one is an exponential fitting curve with an R-squared value of 0.99 and another is a linear fitting curve with an R-squared value of 0.88. The results demonstrated that the combination of fluorescence spectroscopy and PCA can be used for the classification of paper samples with different ages. Moreover, the first-derivative fluorescence spectral curves can be used to quantitatively evaluate the age-related changes of paper samples. [ABSTRACT FROM AUTHOR]

The article by Davies et al. discusses scientific studies on microbially induced sedimentary structures (MISS). The sedimentary structures are caused by benthic microbes in clastic deposits and have the potential to fossilize. In the opinion of Davies et al., it may be problematic to distinguish MISS-like structures (e.g., ‘wrinkle structures’) from similar, but abiotic structures. Therefore, their article argues, there exists a need for an umbrella classification summarizing both biological and abiotic sedimentary structures. However, Davies et al.'s paper does not reflect a thorough understanding of the formation and preservation of MISS. More so, while the authors appreciate the benefit of geological field work, and petrological as well as geochemical laboratory sample analyses, their article appears to widely disregard that this scientific rigor is presented in studies on MISS. The lack of data make the classification by Davies et al. unsupported and difficult to use. It potentially confuses by producing multiple classification possibilities rather than a pragmatic organization, and it contributes an unnecessary terminological ballast to a research area that has progressed far beyond to the concepts that the paper by Davies et al. presents. [ABSTRACT FROM AUTHOR]

This EAACI position paper aims at providing a state-of-the-art overview on nonallergic rhinitis ( NAR). A significant number of patients suffering from persistent rhinitis are defined as nonallergic noninfectious rhinitis ( NANIR) patients, often denominated in short as having NAR. NAR is defined as a symptomatic inflammation of the nasal mucosa with the presence of a minimum of two nasal symptoms such as nasal obstruction, rhinorrhea, sneezing, and/or itchy nose, without clinical evidence of endonasal infection and without systemic signs of sensitization to inhalant allergens. Symptoms of NAR may have a wide range of severity and be either continuously present and/or induced by exposure to unspecific triggers, also called nasal hyperresponsiveness ( NHR). NHR represents a clinical feature of both AR and NAR patients. NAR involves different subgroups: drug-induced rhinitis, (nonallergic) occupational rhinitis, hormonal rhinitis (including pregnancy rhinitis), gustatory rhinitis, senile rhinitis, and idiopathic rhinitis ( IR). NAR should be distinguished from those rhinitis patients with an allergic reaction confined to the nasal mucosa, also called 'entopy' or local allergic rhinitis ( LAR). We here provide an overview of the current consensus on phenotypes of NAR, recommendations for diagnosis, a treatment algorithm, and defining the unmet needs in this neglected area of research. [ABSTRACT FROM AUTHOR]

The division of the world into 'developing' and 'developed' countries has grown increasingly problematic in the past decades. Nonetheless, it remains embedded in legal documents, foreign policy discourse, and colloquial use. This paper explores this complexity by unpacking the different ways in which the 'developing' label is used in the international system. It argues that understanding the complexity around its use requires a rigorous analysis of the label's diverse meanings and consequences. This is done by introducing a taxonomy that intersects two elements: (1) the source of the classification (External or Internal) and (2) the kind of hierarchy implied in the classification (Narrow or Broad). This two-by-two matrix generates four approaches in which the 'developing' vs 'developed' dichotomy is used: Technocratic, Elective, Northern Gaze, and Southern Solidarity. Each approach is explored empirically, illustrated by cases connected to international organizations and multilateral treaties. In doing so, the paper teases out the underlying reasons why the use of the dichotomy is so challenging, based on what kinds of contestation it generates, and which actors are pushing for demise or longevity (and where). [ABSTRACT FROM AUTHOR]

The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm's optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods. [ABSTRACT FROM AUTHOR]

The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands' health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution was applied to Xception to achieve lightweighting, and Inception-ResNet introduces residual connections to accelerate model convergence. However, existing improvements for the Inception module often neglect further enhancement of its receptive field, while increasing the receptive field of CNNs has been widely studied and proven to be effective in improving classification performance. Motivated by this fact, three effective expansion modules are proposed in this paper. The first expansion module, Inception expand (Inception-e) module, is proposed to improve the classification accuracy by concatenating more and deeper convolutional branches. To reduce the number of parameters for Inception e, this paper proposes a second expansion module—Equivalent Inception-e (Eception) module, which is equivalent to Inception-e in terms of feature extraction capability, but which suppresses the growth of the parameter quantity brought by the expansion by effectively reducing the redundant convolutional layers; on the basis of Eception, this paper proposes a third expansion module—Lightweight Eception (Lception) module, which crosses depthwise convolution with ordinary convolution to further effectively reduce the number of parameters. The three proposed modules have been validated on the Cifar10 dataset. The experimental results show that all these extensions are effective in improving the classification accuracy of the models, and the most significant effect is the Lception module, where Lception (rank = 4) on the Cifar10 dataset improves the accuracy by 1.5% compared to the baseline model (Inception module A) by using only 0.15 M more parameters. [ABSTRACT FROM AUTHOR]

In this paper, we study the novel notion of thin polytopes: lattice polytopes whose local |$h^{*}$| -polynomials vanish. The local |$h^{*}$| -polynomial is an important invariant in modern Ehrhart theory. Its definition goes back to Stanley with fundamental results achieved by Karu, Borisov, and Mavlyutov; Schepers; and Katz and Stapledon. The study of thin simplices was originally proposed by Gelfand, Kapranov, and Zelevinsky, where in this case the local |$h^{*}$| -polynomial simply equals its so-called box polynomial. Our main results are the complete classification of thin polytopes up to dimension 3 and the characterization of thinness for Gorenstein polytopes. The paper also includes an introduction to the local |$h^{*}$| -polynomial with a survey of previous results. [ABSTRACT FROM AUTHOR]