Reviewerly/exHarmony huggingface dataset:
Blockchain technology: A potential tool for the management of pharma supply chain

The pharma supply chain comprises various parties including distributors, manufacturers, raw material suppliers, regulators, pharmacies, hospitals, and patients. Due to the product's complexity and transaction flows, an efficient traceability system is needed in the pharma supply chain to identify the current and all previous product owners. Digitizing the track and trace process significantly improves regulatory oversight and guarantees product quality. A distributed platform for shared data that is immutable, trustworthy, accountable, and transparent in the pharmaceutical supply chain could be built using blockchain-based drug traceability. This review aims to shed light on blockchain technology's significance and necessity for pharmaceutical supply chain management systems. A comprehensive literature review was performed between January 2017 and September 2023. The search was conducted to elaborate on blockchain technology. Blockchain is a software-based technology that logs and records transactions using a block structure arranged chronologically. Cryptography technology links and secures these blocks on a peer-to-peer network. Blockchain is anticipated to transform the pharmaceutical supply chain by giving all participants access to a single, straightforward system that provides transparency, security, and oversight of the end-to-end delivery of goods. In all, various literature data were included in this review. Using a supply chain powered by blockchain has many benefits. To begin with, it gives a thorough account of the entire procedure from start to finish. A single piece of software can manage the entire supply chain. Additionally, it increases communication between parties with permission. The enhanced security and traceability that blockchain offers is another important benefit. A blockchain system can track, trace, and recall products. Blockchain-based pharmaceutical supply chain management enables the tracking of medicinal drug transactions from raw materials suppliers to end consumers. The pharma blockchain has the potential to enhance the security, integrity, data provenance, and functionality of the supply chain due to its transparency, immutability, and auditability.
Enabling Data Discovery with the Astrobiology Resource Metadata Standard

As scientific investigations increasingly adopt Open Science practices, reuse of data becomes paramount. However, despite decades of progress in internet search tools, finding relevant astrobiology datasets for an envisioned investigation remains challenging due to the precise and atypical needs of the astrobiology researcher. In response, we have developed the Astrobiology Resource Metadata Standard (ARMS), a metadata standard designed to uniformly describe astrobiology "resources," that is, virtually any product of astrobiology research. Those resources include datasets, physical samples, software (modeling codes and scripts), publications, websites, images, videos, presentations, and so on. ARMS has been formulated to describe astrobiology resources generated by individual scientists or smaller scientific teams, rather than larger mission teams who may be required to use more complex archival metadata schemes. In the following, we discuss the participatory development process, give an overview of the metadata standard, describe its current use in practice, and close with a discussion of additional possible uses and extensions.
Implementing and Testing a U-Space System: Lessons Learnt

Within the framework of the European Union’s Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of operations (ConOps) provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic Conflict Resolution, Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Moreover, a Web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their tasks. The development and testing of the environment provided lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles.
How to create and use a national cross-domain ontology and data infrastructure on the Semantic Web

This paper presents a model and lessons learned for creating a cross-domain national ontology and Linked (Open) Data (LOD) infrastructure. The idea is to extend the global, domain agnostic “layer cake model” underlying the Semantic Web with domain specific and local features needed in applications. To test and demonstrate the infrastructure, a series of LOD services and portals in use have been created in 2002–2023 that cover a wide range of application domains. They have attracted millions of users in total suggesting feasibility of the proposed model. This line of research and development is unique due to its systematic national level nature and long time span of over twenty years.
Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application

Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health issues. In the realm of health diagnostics, an often overlooked but critical aspect is the consideration of cost-sensitive learning, a facet that this study prioritizes over the non-invasive nature of the diagnostic process whereas the other standard metrics such as accuracy and sensitivity reflect weakness in error profile.This research aims to investigate the total cost of misclassification (Total Cost) by decision rule Machine Learning (ML) algorithms implemented in Java platforms such as DecisionTable, JRip, OneR, and PART. An augmented dataset with conjunctiva images along candidates' demographic and anthropometric features under supervised learning is considered with a specific emphasis on cost-sensitive classification.The opted decision rule classifiers use the text features, additionally the image feature 'a* value of CIELAB color space' extracted from the conjunctiva digital images as input attributes. The pre-processing consists of amalgamating text and image features on a uniform scale, normalizing. Then the 10-fold cross-validation enables the classification of samples into two categories: the presence or absence of the anemia. This study utilizes the Cost Ratio (ρ) extracted from the cost matrix to meticulously monitor the Total Cost in four different cost ratio methodologies namely Uniform (U), Uniform Inverted (UI), Non-Uniform (NU), and Non-Uniform Inverted (NUI).It has been established that the PART classifier stands out as the top performer in this binary classification task, yielding the lowest mean total cost of 629.9 compared to other selected classifiers. Moreover, it demonstrates a comparatively lower standard deviation 335.9, and lower total cost across all four different cost ratio methodologies. The ranking of algorithm performance goes as follows: PART, JRIP, DecisionTable, and OneR.The significance of adopting a cost-sensitive learning approach is emphasized showing the PART classifier's consistent performance within the proposed framework for learning the anemia dataset. This emphasis on cost-sensitive learning not only enhances the recommendations in diagnosis but also holds the potential for substantial cost savings and makes it a noteworthy focal point in the advancement of AI-driven health care.
VSDHS-CIEA: Color image encryption algorithm based on novel variable-structure discrete hyperchaotic system and cross-plane confusion strategy

Recently, the color image encryption algorithm based on chaos theory has become the focus of current research. When encrypting color images, the common practice is to treat color images as different gray components and process them severally, which results in more redundancy and low efficiency. The security of chaotic cryptosystems also depends on the performance of the chaotic systems adopted. The structures of many current chaotic systems are fixed, making their behaviors highly predictable. Additionally, the range of chaotic region parameters is limited and discontinuous. To solve the above-mentioned problems, a new color image encryption algorithm (CIEA) using fractal and chaos theory is presented, which fully considers the inherent connection among the RGB components of color images. First, we propose a variable-structure discrete hyperchaotic system (VSDHS) to solve the dilemma encountered by existing chaotic systems. The excellent dynamic properties of VSDHS are verified by rigorous mathematical proof and simulation performance analyses. Then, VSDHS-CIEA is designed using fractal theory and VSDHS. The algorithm makes full use of the inherent connection among the different components of color images and performs cross-plane confusion. Simulations and performance analyses prove that VSDHS-CIEA has higher security and better performance than some representative image encryption algorithms.
Data-driven multinomial random forest: a new random forest variant with strong consistency

Abstract In this paper, we modify the proof methods of some previously weakly consistent variants of random forest into strongly consistent proof methods, and improve the data utilization of these variants in order to obtain better theoretical properties and experimental performance. In addition, we propose the Data-driven Multinomial Random Forest (DMRF) algorithm, which has the same complexity with BreimanRF (proposed by Breiman) while satisfying strong consistency with probability 1. It has better performance in classification and regression tasks than previous RF variants that only satisfy weak consistency, and in most cases even surpasses BreimanRF in classification tasks. To the best of our knowledge, DMRF is currently a low-complexity and high-performing variation of random forest that achieves strong consistency with probability 1.
What can machine vision do for lymphatic histopathology image analysis: a comprehensive review

Abstract Over the past 10 years, machine vision (MV) algorithms for image analysis have been developing rapidly with computing power. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide diagnostic references to doctors. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the application of image processing techniques based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some potential methods are proposed, and further prospects are made.
New improved model for joint segmentation and registration of multi-modality images: application to medical images

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively.
Personalized multi-attribute recommendation method based on partial order set

With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results.
Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.
Robustness test-time augmentation via learnable aggregation and anomaly detection

Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models.
Slender loris optimization algorithm for solving optimization problems

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms.
Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.
Bi-DNE: bilayer evolutionary pattern preserved embedding for dynamic networks

Abstract Network embedding is a technique used to generate low-dimensional vectors representing each node in a network while maintaining the original topology and properties of the network. This technology enables a wide range of learning tasks, including node classification and link prediction. However, the current landscape of network embedding approaches predominantly revolves around static networks, neglecting the dynamic nature that characterizes real social networks. Dynamics at both the micro- and macrolevels are fundamental drivers of network evolution. Microlevel dynamics provide a detailed account of the network topology formation process, while macrolevel dynamics reveal the evolutionary trends of the network. Despite recent dynamic network embedding efforts, a few approaches accurately capture the evolution patterns of nodes at the microlevel or effectively preserve the crucial dynamics of both layers. Our study introduces a novel method for embedding networks, i.e., bilayer evolutionary pattern-preserving embedding for dynamic networks (Bi-DNE), that preserves the evolutionary patterns at both the micro- and macrolevels. The model utilizes strengthened triadic closure to represent the network structure formation process at the microlevel, while a dynamic equation constrains the network structure to adhere to the densification power-law evolution pattern at the macrolevel. The proposed Bi-DNE model exhibits significant performance improvements across a range of tasks, including link prediction, reconstruction, and temporal link analysis. These improvements are demonstrated through comprehensive experiments carried out on both simulated and real-world dynamic network datasets. The consistently superior results to those of the state-of-the-art methods provide empirical evidence for the effectiveness of Bi-DNE in capturing complex evolutionary patterns and learning high-quality node representations. These findings validate the methodological innovations presented in this work and mark valuable progress in the emerging field of dynamic network representation learning. Further exploration demonstrates that Bi-DNE is sensitive to the analysis task parameters, leading to a more accurate representation of the natural evolution process during dynamic network embedding.