Reviewerly/exHarmony huggingface dataset:
How Can Blockchain Technology Be Used to Manage the COVID-19 Vaccine Supply Chain? A Systematic Literature Review and Future Research Directions

To ensure the success of the COVID-19 vaccination program, vaccine supply networks must become more efficient, secure, and dependable. This paper provides a systematic literature review of current academic work on blockchain-based COVID-19 supply chain management (CVSCM), addressing the role of blockchain in CVSCM and its challenges. The paper's objectives are to comprehensively analyze the literature on blockchain solutions in the CVSCM and propose a future research agenda based on gaps in the present literature. The systematic literature review involved 34 peer-reviewed journal and conference publications published between 2019 and 2022. Using a thematic analysis, we observed that the public blockchain is the most often-used blockchain platform for constructing the CVSCM frameworks. The supply chain data privacy and security are major driving factors. Blockchain technology significantly affects CVSCM by allowing for distributed transaction execution and verification. Blockchain technology enables traceability, digitalization, disintermediation of the supply chain, and enhanced data privacy. However, several challenges were identified, including privacy worries, excessive energy consumption, latency, transactional throughput, and scalability. Our results provide the groundwork for future research aimed at increasing technical integration in blockchain supply chain solutions, cross-chain interoperability, and scalability, the feasibility of commercial applications in real-world industrial settings, data security, and privacy. Future research might also closely monitor emerging technologies in CVSCM, such as edge computing, virtual reality, machine learning, artificial intelligence, and blockchain advancements, and provide more impartial support to the many research potentials discussed.
Cobalt Prospectivity Using a Conceptual Fuzzy Logic Overlay Method Enhanced with the Mineral Systems Approach

Abstract This paper describes mineral prospectivity research conducted in Finland to predict favorable areas for cobalt exploration using the “fuzzy logic overlay” method in a GIS platform and public geodata of the Geological Survey of Finland. Cobalt occurs infrequently as a core product in mineral deposits. Therefore, we decided to construct separate conceptual mineral prospectivity models within the Northern Fennoscandian Shield, Finland, for four deposit types: (1) “ Orthomagmatic Ni–Cu–Co sulfide deposits, ” (2) “ Outokumpu-type mantle peridotite-associated volcanogenic massive sulfide (VMS)-style Cu–Co–Zn–Ni–Ag–Au deposits, ” (3) “ Talvivaara black shale-hosted Ni–Zn–Cu–Co-type deposits” and (4) “Kuusamo-type (orogenic gold with atypical metal association) Au–Co–Cu–U–LREE deposits ”. In addition, we created a model combining till geochemical data with data derived from bedrock drilling and mineral indications, including boulders and outcrops. The mineral prospectivity models were statistically tested with the “ receiver operating characteristics ” method using exploration drilling data from known mineral deposits as validation sites. In addition, the predictive performance of the models was evaluated by using success rate curves, where the number of previously identified deposits was compared with the area coverage of the predicted highly favorable areas. These results indicate that the knowledge-driven mineral prospectivity method using parameters derived from mineral systems models is effective in defining favorable exploration target areas at the regional scale. This study's innovation lies in its comprehension of the process of evaluating mineral prospectivity when the commodity of interest is not the primary commodity within the mineral system.
Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks

In recent years, the application of artificial intelligence (AI) in the automotive industry has led to the development of intelligent systems focused on road safety, aiming to improve protection for drivers and pedestrians worldwide to reduce the number of accidents yearly. One of the most critical functions of these systems is pedestrian detection, as it is crucial for the safety of everyone involved in road traffic. However, pedestrian detection goes beyond the front of the vehicle; it is also essential to consider the vehicle's rear since pedestrian collisions occur when the car is in reverse drive. To contribute to the solution of this problem, this research proposes a model based on convolutional neural networks (CNN) using a proposed one-dimensional architecture and the Inception V3 architecture to fuse the information from the backup camera and the distance measured by the ultrasonic sensors, to detect pedestrians when the vehicle is reversing. In addition, specific data collection was performed to build a database for the research. The proposed model showed outstanding results with 99.85% accuracy and 99.86% correct classification performance, demonstrating that it is possible to achieve the goal of pedestrian detection using CNN by fusing two types of data.
A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.
Contactless Multi-User Virtual Hair Design Synthesis

This study introduces a virtual reality (VR)-based remote hair design training system that addresses the limitations of physical presence. By leveraging virtual environments and eliminating the need for tangible tools, this system allows hairstylists and learners to collaborate and master hair-design techniques from a distance. In this approach, a section of a user’s hair was derived from their photograph, and during the training, the most compatible 3D hair model was integrated with the user’s 3D avatar using the recommended matching procedure. This aligned hairstyle was subsequently incorporated into a 3D model for hair simulation. Further, VR HMD interaction mapping was used for hair cutting and styling. A collaborative environment for hair design has been crafted, enabling multiple participants to partake in remote hairstyle education. This system offers cost-effective training, enhances cooperative learning, and aligns with the requirements of contact-free education. In essence, this research has transformed remote hair design training with immersive VR technology. To verify the system’s accuracy, tests were conducted, and the results showcased enhancements in the matching procedure.
Semantic Encapsulation using Linking Types

Interoperability pervades nearly all mainstream language implementations, as most systems leverage subcomponents written in different languages. And yet, such linking can expose a language to foreign behaviors that are internally inexpressible, which poses a serious threat to safety invariants and programmer reasoning. To preserve such invariants, a language may try to add features to limit the reliance on external libraries, but endless extensions can obscure the core abstractions the language was designed to provide.
Comparative Histomorphological Study of Spleen in Goat, Sheep, Cow and Buffalo

Immunity in some animals, such as goats, stands for unique properties by which they withstand unpleasant environments. Spleen influences animals' immunity through its hematopoietic and immunological roles. Because of that, we emphasized the average morphological and histological properties of the Goat, Sheep, Cow and Buffalo spleen. We randomly collected three spleens for every species from healthy slaughtered Goats, Sheep, Cows and Buffalo. All the animals were adult and weighed about 71, 80, 400 and 500 grams, respectively. Histological cutting was followed according to the paraffin method; then, the samples were ready for microscopic exam. The structure results revealed that the spleen of animals is supported externally by a thick fibromuscular connective tissue capsule, which vascularizes by subcapsular sinus. Stromal trabeculae emerge from the capsule, entering the splenic parenchyma. The parenchyma of animals' spleen differentiates into white pulp, which organizes into the periarterial lymphatic sheath along with lymph nodules, red pulp ropes in splenic sinuses among splenic cords, and marginal zone in which macrophage occupies excellent importance. This study concluded that the amount of smooth muscle in the spleen septa in goats was more than in the rest of the experimental animals. Keywords: Histomorphology; Spleen; Goat; Sheep; Cow; Buffalo
AI4Architect: An Intelligent Help System to Support Students in the Design Domain

Chatbots are important tools for improving learning by assisting students in their daily learning activities. In recent years, Deep Learning techniques have made it possible to train chatbots in increasingly comprehensive and even adaptive ways. ChatGPT and DALL-E are the latest and most advanced examples of chatbots that are capable, respectively, of providing textual responses to questions about almost any knowledge domain and of creating an image produced by a concept expressed in a textual form. More and more, students use such applications as a support in their learning process at all levels of study. In this paper, we present the AI4Architect system, a web application to support students in creating their projects in the design domain. The system consists of two modules, structured in a pipe configuration. The first module is based on ChatGPT and the second on DALL-E: students can first post a question and then ask for one or more images to be created based on the response to that question. Our goal is to investigate if such an AI-based system can help students as draw guidance and inspiration for the production of their design projects. A controlled experiment was conducted that showed the effectiveness of the system as a valid educational support.
Task-Specific and Graph Convolutional Network based Multi-modal Movie Recommendation System in Indian Setting

Nowadays the Recommendation System, a subclass of information filtering system does not require any introduction, and the movie recommendation system plays a vital role in the streaming platform where many movies are needed to analyze before showcasing a perfectly matched subset of them to its users. Most of the available datasets contain the rating information of user-movie pairs and this is the reason of regression-based works that predict the rating value for a user-movie pair. We have also found that there is no work on the Indian regional language-based dataset containing no users’ feedback in the rating scale. In this paper, we have introduced a recommendation system for the Indian language-based multi-modal Hindi movies’ dataset where users’ feedback is from the three different classes, i) Dislike, ii) Like, and iii) Neutral. Here, we have used the Flickscore dataset and added the audio-video information of the trailers of its movies for making it multi-modal. Besides that, we have investigated the performance of a classification-based model having two modules, (i) Task-Specific (TS) and (ii) Graph Convolutional Network (GCN). The performance of different combinations of these modules is tested on different modalities of the dataset. We have tested its performance in cold-start scenarios also. Modality wise different embedding processes have been introduced here and the experimental results tried to conclude how the model works in uni-modal, bi-modal, and all-modal information of movies in an information system where no rating information is present.
Violence Detection in Real-Life Audio Signals Using Lightweight Deep Neural Networks

The automatic detection of violent behavior in acoustic data has become an important research area due to its growing application potential in various surveillance and behavior monitoring tasks where it causes fewer privacy issues than video. In this paper, we propose two deep learning approaches of different characteristics to classify speech data containing violent behavior against data that only includes patterns of non-violent behavior. The first approach is based on conventional deep neural networks, while the second approach uses lightweight deep neural networks. Both utilize Mel-spectrogram images of speech signals as input to fine-tuned models. Our best lightweight model's classification accuracy is about 8% better than the previous state-of-the-art result using the same benchmark data. Lightweight models have fewer parameters and require fewer computing resources than conventional deep models. This can be an important advantage when being deployed on mobile or edge devices.
AugDMC: Data Augmentation Guided Deep Multiple Clustering

Clustering aims to group similar objects together while separating dissimilar ones apart. Thereafter, structures hidden in data can be identified to help understand data in an unsupervised manner. Traditional clustering methods such as k-means provide only a single clustering for one data set. Deep clustering methods such as auto-encoder based clustering methods have shown a better performance, but still provide a single clustering. However, a given dataset might have multiple clustering structures and each represents a unique perspective of the data. Therefore, some multiple clustering methods have been developed to discover multiple independent structures hidden in data. Although deep multiple clustering methods provide better performance, how to efficiently capture the alternative perspectives in data is still a problem. In this paper, we propose AugDMC, a novel data Augmentation guided Deep Multiple Clustering method, to tackle the challenge. Specifically, AugDMC leverages data augmentations to automatically extract features related to a certain aspect of the data using a self-supervised prototype-based representation learning, where different aspects of the data can be preserved under different data augmentations. Moreover, a stable optimization strategy is proposed to alleviate the unstable problem from different augmentations. Thereafter, multiple clusterings based on different aspects of the data can be obtained. Experimental results on three real-world datasets compared with state-of-the-art methods validate the effectiveness of the proposed method.
An automatic vulnerability classification framework based on BiGRU-TextCNN

Common Vulnerabilities and Exposures (CVE) records known vulnerabilities and provides standardized descriptions. By utilizing Common Weakness Enumeration (CWE) to classify vulnerabilities, it can provide richer background knowledge and more detailed mitigation measures for the vulnerability. However, due to the negligence on manual classification and the evolution of vulnerabilities, the accuracy of vulnerabilities classification needs urgent improvement. Additionally, the large and ever-increasing number of vulnerabilities poses a huge challenge to the efficiency and accuracy of vulnerabilities manual classification. To address that, we propose a vulnerability classification framework based on BiGRU-TextCNN, which processes, trains, predicts to automatically classify vulnerabilities into weaknesses based on the description of vulnerability. To verify the performance and feasibility of the proposed framework, we first conducting comparison experiments on different text classification models, and then predicting the corresponding weakness with the description of vulnerability utilizing the proposed framework.
Challenges of the Creation of a Dataset for Vision Based Human Hand Action Recognition in Industrial Assembly

This work presents the Industrial Hand Action Dataset V1, an industrial assembly dataset consisting of 12 classes with 459,180 images in the basic version and 2,295,900 images after spatial augmentation. Compared to other freely available datasets tested, it has an above-average duration and, in addition, meets the technical and legal requirements for industrial assembly lines. Furthermore, the dataset contains occlusions, hand-object interaction, and various fine-grained human hand actions for industrial assembly tasks that were not found in combination in examined datasets. The recorded ground truth assembly classes were selected after extensive observation of real-world use cases. A Gated Transformer Network, a state-of-the-art model from the transformer domain was adapted, and proved with a test accuracy of 86.25% before hyperparameter tuning with 18,269,959 trainable parameters, that it is possible to train sequential deep learning models with this dataset.
Platform Based on Augmented Reality to Support Cultural Tourism in the Department of Cesar, Colombia

The tourism sector is one of the sectors that have been most affected by the Covid-19 pandemic, due to the reduction in its income by more than half in 2020 compared to the previous year, according to the UNWTO. In Colombia, the panorama does not differ, with fall in sales close to 70% compared to 2019 in the hotel sector and travel agencies. Even so, it is one of the sectors to which many regions point for post-pandemic economic recovery. So, in the context of the current Covid-19 contingency, it is necessary to redefine the tourist experience. Various international organizations and/or authors propose innovation and digital transformation as key strategies for the tourism sector’s resilience. Policies for economic reactivation at the national level bet on the promotion of tourism, strengthening itself through CTI activities and promoting the integration of the private sector and academia. This article shows the development of an application based on augmented reality that is the first software application that promotes cultural tourism in the department of Cesar. For the development of this application, agile development methodologies were used that allow direct interaction with the client, which allowed a significant impact on economic development in the department of Cesar.
Hybrid image encryption algorithm based on compressive sensing, gray wolf optimization, and chaos

Growing reliance on digital communications has necessitated development of dependable and secure technologies to ensure that the transmission and reception of images over the Internet do not pose a risk to the data of individuals and governments. We propose developing a hybrid image encryption and compression algorithm by combining compressive sensing, the gray wolf algorithm, and multi-dimensional chaotic systems. It aims to generate a highly secure encrypted image while conserving transmission and storage resources. This algorithm overlaps several stages designed to protect vital images while minimizing size. First, the image is converted to the frequency domain using the discrete wavelet transform. Then, the discrete wavelet transform coefficients are scrambled globally using the Waleed-Ali Map and the gray wolf algorithm. Second, the confused image is measured by a parameters-controlled matrix to reduce transmission costs. The final encrypted image is obtained after performing the diffusion operation with a bitstream derived from the Nahrain chaotic map. The average peak signal-to-noise ratio score was 53.1995, and the average mean squared error score was 0.6130, demonstrating that the plaintext and decrypted images are identical. The average correlation coefficient score was −0.010095; the average entropy analysis was 7.9987; and the average number of pixel change rate and unified average changing intensity analyses were 99.60 and 33.52, respectively. The experimental results demonstrate the algorithm's efficiency and robustness, as well as the high quality of the reconstructed image.
Galaxy Morphology Classification Using a Semi-supervised Learning Algorithm Based on Dynamic Threshold

Abstract Machine learning has become a crucial technique for classifying the morphology of galaxies as a result of the meteoric development of galactic data. Unfortunately, traditional supervised learning has significant learning costs since it needs a lot of labeled data to be effective. FixMatch, a semi-supervised learning algorithm that serves as a good method, is now a key tool for using large amounts of unlabeled data. Nevertheless, the performance degrades significantly when dealing with large, imbalanced data sets since FixMatch relies on a fixed threshold to filter pseudo-labels. Therefore, this study proposes a dynamic threshold alignment algorithm based on the FixMatch model. First, the class with the highest amount has its reliable pseudo-label ratio determined, and the remaining classes’ reliable pseudo-label ratios are approximated in accordance. Second, based on the predicted reliable pseudo-label ratio for each category, it dynamically calculates the threshold for choosing pseudo-labels. By employing this dynamic threshold, the accuracy bias of each category is decreased and the learning of classes with less samples is improved. Experimental results show that in galaxy morphology classification tasks, compared with supervised learning, the proposed algorithm significantly improves performance. When the amount of labeled data is 100, the accuracy and F1-score are improved by 12.8% and 12.6%, respectively. Compared with popular semi-supervised algorithms such as FixMatch and MixMatch, the proposed algorithm has better classification performance, greatly reducing the accuracy bias of each category. When the amount of labeled data is 1000, the accuracy of cigar-shaped smooth galaxies with the smallest sample is improved by 37.94% compared to FixMatch.