Reviewerly/exHarmony huggingface dataset:
Privacy preserving transparent supply chain management through Hyperledger Fabric

The revolution of blockchain technology started in the form of a cryptocurrency called Bitcoin. In recent years, this decentralized technology has attained worldwide adoption and growth in multiple sectors, including e-governance, e-commerce, and asset management. Having disrupted these sectors, the choice of blockchain technology to solve real-world problems concerning supply chain seems to be an innovative strategy. In this paper, an attempt is made to analyze blockchain's ability to improve the standards of supply chain management. This paper includes a methodology to implement the suggested idea using a permissioned blockchain platform—Hyperledger Fabric. For the paper's scope, the existing supply chain problems, such as data integrity, provenance transparency, privacy, and security, are given emphasis more specifically in the context of the coffee supply chain industry, while also concurrently attempting to generalize the solution to manage other supply chain activities effectively. Thus, the final objective of this research is to identify whether permissioned blockchain platforms could help stakeholders in the supply chain industry engage in a less-corruptible alternative to traditional web technology and whether they could enable a more positively nuanced blockchain system that draws the best balance between traditional web technology and a public blockchain, including privacy protection and security.
Sample preparation for geochemical analysis: Strategies and significance

Accurate determination of elemental and isotopic compositions of geological samples must be a pre-requisite for successful mineral exploration, basic geochemical and geochronological studies. During rock analysis, sample digestion which involves steps from simple dilution to partial or total digestions, is a crucial step and is the limiting factor many times for achieving reproducible and accurate analytical results. Sample preparation is a critical step in the geochemical analysis, and complete sample dissolution is a pre-requisite for obtaining accurate and precise data for geological materials in most studies. Sample matrix, analytes of interest, the analytical technique, and intended purpose play a major role in selecting the optimum sample preparation method. This review focuses on the recent advances, developments, and trends in sample digestion methods, such as open-vessel acid digestion, closed digestions, microwave digestion, alkali fusion, use of NH4F for both open and closed digestions, sintering with Na2O2, fire-assay methods, infrared radiation digestions, high-pressure asher digestions, and automated sample preparation systems (based on artificial intelligence) along with examples of some important and exciting applications in geochemical and geochronological studies. Various sample decomposition methods have been compared to bring out advantages and limitations for a particular application.
Predicting Breast Cancer Based on Optimized Deep Learning Approach

Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras-Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and K-nearest neighbor algorithm (KNN) were compared with the optimized deep RNN. The regular ML models and the optimized deep RNN have been applied the selected features. The results showed that the optimized deep RNN with the selected features by univariate has achieved the highest performance for CV and the testing results compared to the other models.
Gamma-ray protection capacity evaluation and satellite data based mapping for the limestone, charnockite, and gneiss rocks in the Sirugudi taluk of the Dindigul district, India

The remote sensing techniques for investigation and mapping of mineral resources along with the lithological variations have become a leading and optimistic way in recent years. Sirugudi taluk of Dindigul district, Tamil Nadu, is identified with many quarries of limestone and charnockite that have economic importance since that is used in the cement and construction industry. The present study validates the radiation shielding properties of limestone, charnockite, and gneiss rock and the mapping through remote sensing techniques. Several images processing techniques were performed using the Advanced Spaceborne Thermal Emission and Reflection Radiometer data to delineate the boundaries of lithology at Sirugudi. The laboratory-based reflectance spectra obtained for the limestone, charnockite, and gneiss sample in the range of 350 nm–2500 nm shows a remarkable correlation with the significant oxides of the samples. The radiation shielding performance for the present rocks was investigated using the Monte Carlo simulation and the theoretical online program XCOM. The three investigated rock types’ linear attenuation coefficient (μ) values are at their maximum at low energies, and the μ rapidly decreases as energy increases in the low energy range. For gneiss, the μ decreases from 32.484 cm−1 to 4.731 cm−1 between 0.015 and 0.030 MeV. At all investigated energies, the μ values follow the order of Charnockite > Gneiss > Limestone. Based on the simulated linear attenuation coefficient values, the half-value layer, radiation protection efficiency, and the thickness equivalent were estimated. The estimated results demonstrated that thinner rock thicknesses are needed to shield the incoming photons at lower energies. The half-value thicknesses are 0.021, 0.026, and 0.018 cm for Gneiss, Limestone, and Charnockite at 15 keV.
Number of Autonomous Cognitive Agents in a Neural Network

This paper considers several aspects of the relationship between size, structure, speed of propagation and the number of autonomous cognitive agents in a neural network. While memory and function generation capacities of neural networks with scale-invariant structure have been investigated extensively, the number of autonomous agents, associated with the simultaneous processing of the independent components of the data, has not received prior attention. We propose the emergence of the dichotomy of causal and noncausal regions created by the speed of propagation, in which the autonomous cognitive agents are not bound in a causal relationship with other agents. Arguments are presented for why the count of autonomous agents is best estimated with respect to the dimensionality of the underlying space. The number of autonomous agents obtained for the human brain equals 25, and it is significant that the number in the sub-system modules also turns out to be close to the same value. This number equals the number of enumerative categories of reality in at least one philosophical tradition, and this coincidence may be attributed to the capacity of consciousness to reflect on its own structure. It is possible that near equality across layers provides a special uniqueness to the human brain. We propose that the findings of this study will be useful in the design of neural-network-based AI systems that are designed to emulate human cognitive capacity.
Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine

Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift. The objective was to characterize the impact of temporal dataset shift on clinical prediction models and benchmark DG and UDA algorithms on improving model robustness. In this cohort study, intensive care unit patients from the MIMIC-IV database were categorized by year groups (2008-2010, 2011-2013, 2014-2016 and 2017-2019). Tasks were predicting mortality, long length of stay, sepsis and invasive ventilation. Feedforward neural networks were used as prediction models. The baseline experiment trained models using empirical risk minimization (ERM) on 2008-2010 (ERM[08-10]) and evaluated them on subsequent year groups. DG experiment trained models using algorithms that estimated invariant properties using 2008-2016 and evaluated them on 2017-2019. UDA experiment leveraged unlabelled samples from 2017 to 2019 for unsupervised distribution matching. DG and UDA models were compared to ERM[08-16] models trained using 2008-2016. Main performance measures were area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve and absolute calibration error. Threshold-based metrics including false-positives and false-negatives were used to assess the clinical impact of temporal dataset shift and its mitigation strategies. In the baseline experiments, dataset shift was most evident for sepsis prediction (maximum AUROC drop, 0.090; 95% confidence interval (CI), 0.080-0.101). Considering a scenario of 100 consecutively admitted patients showed that ERM[08-10] applied to 2017-2019 was associated with one additional false-negative among 11 patients with sepsis, when compared to the model applied to 2008-2010. When compared with ERM[08-16], DG and UDA experiments failed to produce more robust models (range of AUROC difference, - 0.003 to 0.050). In conclusion, DG and UDA failed to produce more robust models compared to ERM in the setting of temporal dataset shift. Alternate approaches are required to preserve model performance over time in clinical medicine.
Open Innovation of Institutional Investors and Higher Education System in Creating Open Approach for SDG-4 Quality Education: A Conceptual Review

The introduction of sustainable development goals has made sustainability a top priority for most nations.This has raised the investment into the educational system for potential growth and for creating an innovation culture in any country; the role of institutional investors in the development of financing clean energy infrastructure, entrepreneurial development, poverty reduction, and driving corporate social responsibility and firm development has been found significant.An intensive literature search of Scopus and the web of science database was explored with different relevant sets of keywords "Institutional investors AND Education", "Institutional investors" AND "Higher education", which drew 29 final articles between 2010-2021.This scoping review draws a new open approach for creating alternative sustainable funding for the higher education system and investment opportunities for institutional investors with government intervention.This research becomes the first that shows the road map to the higher education system in creating alternative funding through the proposed Institutional Investor & Sustainable University Funding Governance Code, which will make the higher education system independent and ease the burden on the government, as well as create a huge market opportunity for both players.This research model will scope the collaborative approach towards sustainable development goal 4-Quality education.
Facebook, Meta, the metaverse and libraries

Purpose By understanding these trends, information professionals can better position themselves to help their patrons as the technology develops, as well as position their organizations for success in the world the metaverse will create. Design/methodology/approach This paper defines core concepts of the metaverse, including defining what it is, explaining the narratives surrounding it and exploring its implications for intellectual property and libraries. Findings The development of the metaverse is poised to change the parameters by which libraries work, with implications for information accessibility as well as possible technological barriers. The same economies of scale and disconnection that could potentially threaten libraries could also be an opportunity to develop new partnerships and frameworks for cooperation, if information professionals choose to help shape the metaverse instead of only react to it. Originality/value Understanding the range of possibilities for how the metaverse might develop helps to navigate the role libraries might play as it emerges.
Collaborative Graph Learning for Session-based Recommendation

Session-based recommendation (SBR) , which mainly relies on a user’s limited interactions with items to generate recommendations, is a widely investigated task. Existing methods often apply RNNs or GNNs to model user’s sequential behavior or transition relationship between items to capture her current preference. For training such models, the supervision signals are merely generated from the sequential interactions inside a session, neglecting the correlations of different sessions, which we argue can provide additional supervisions for learning the item representations. Moreover, previous methods mainly adopt the cross-entropy loss for training, where the user’s ground truth preference distribution towards items is regarded as a one-hot vector of the target item, easily making the network over-confident and leading to a serious overfitting problem. Thus, in this article, we propose a Collaborative Graph Learning (CGL) approach for session-based recommendation. CGL first applies the Gated Graph Neural Networks (GGNNs) to learn item embeddings and then is trained by considering both the main supervision as well as the self-supervision signals simultaneously. The main supervisions are produced by the sequential order while the self-supervisions are derived from the global graph constructed by all sessions. In addition, to prevent overfitting, we propose a Target-aware Label Confusion (TLC) learning method in the main supervised component. Extensive experiments are conducted on three publicly available datasets, i.e., Retailrocket, Diginetica, and Gowalla. The experimental results show that CGL can outperform the state-of-the-art baselines in terms of Recall and MRR.
Transformer-based deep learning models for the sentiment analysis of social media data

Sentiment analysis (SA) is a widely used contextual mining technique for extracting useful and subjective information from text-based data. It applies on Natural Language Processing (NLP), text analysis, biometrics, and computational linguistics to identify, analyse, and extract responses, states, or emotions from the data. The features analysis technique plays a significant role in the development and improvement of a SA model. Recently, GloVe and Word2vec embedding models have been widely used for feature extractions. However, they overlook sentimental and contextual information of the text and need a large corpus of text data for training and generating exact vectors. These techniques generate vectors for just those words that are included in their vocabulary and ignore Out of Vocabulary Words (OOV), which can lead to information loss. Another challenge for the classification of sentiments is that of the lack of readily available annotated data. Sometimes, there is a contradiction between the review and their label that may cause misclassification. The aim of this paper is to propose a generalized SA model that can handle noisy data, OOV words, sentimental and contextual loss of reviews data. In this research, an effective Bi-directional Encoder Representation from Transformers (BERT) based Convolution Bi-directional Recurrent Neural Network (CBRNN) model is proposed with for exploring the syntactic and semantic information along with the sentimental and contextual analysis of the data. Initially, the zero-shot classification is used for labelling the reviews by calculating their polarity scores. After that, a pre-trained BERT model is employed for obtaining sentence-level semantics and contextual features from that data and generate embeddings. The obtained contextual embedded vectors were then passed to the neural network, comprised of dilated convolution and Bi-LSTM. The proposed model uses dilated convolution instead of classical convolution to extract local and global contextual semantic features from the embedded data. Bi-directional Long Short-Term Memory (Bi-LSTM) is used for the entire sequencing of the sentences. The CBRNN model is evaluated across four diverse domain text datasets based on accuracy, precision, recall, f1-score and AUC values. Thus, CBRNN can be efficiently used for performing SA tasks on social media reviews, without any information loss.
Image Enhancement Based on Rough Set and Fractional Order Differentiator

In the paper, an image enhancement algorithm based on a rough set and fractional order differentiator is proposed. By combining the rough set theory with a Gaussian mixture model, a new image segmentation algorithm with higher immunity is obtained. This image segmentation algorithm can obtain more image layers with concentrating information and preserve more image details than traditional algorithms. After preprocessing, the segmentation layers will be enhanced by a new adaptive fractional order differential mask in the Fourier domain. Experimental results and numerical analysis have verified the effectiveness of the proposed algorithm.
Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model

With the inherent volatility of solar radiation, accurate short-term prediction of solar irradiance is essential to cost-efficiently stabilise and operate power grids. Cloud is the major influencing factor for variations in solar irradiance at a minute scale. This study proposes a short-term solar irradiance prediction model based on a deep learning network. Firstly, a new hybrid cloud detection method is proposed to calculate cloud coverage under different sky condition. The relationship between cloud coverage at the current moment and the global horizontal irradiance (GHI) 10 min later is directly analysed to avoid the error of assuming cloud motion as rigid motion. Meteorological parameters with strong correlation with the GHI in ten minutes are admitted into the model input, mainly including relative humidity and Aerosol Optical Depth (AOD) of 500 nm in this study. The GHI of two-time intervals ahead are also selected after autocorrelation analysis to represent other comprehensive impacts. Together with cloud, the meteoritical parameters and the historical GHI are taken as the input of a deep learning network simultaneously, which is based on long short-term memory network (LSTM) and optimised by Bayesian Optimisation (BO). The solar irradiance after ten minutes is the output of the model. Experiments were conducted to evaluate the performance of the proposed forecast model comparing with some benchmark models. Results showed that the proposed model outperformed other models with a normalised root mean square error (nRMSE) of 15.25% under all sky conditions. An improvement of 8.23% was reached compared with persistent model.
A velocity-based butterfly optimization algorithm for high-dimensional optimization and feature selection

• A velocity-based butterfly optimization algorithm is proposed. • A modified position updated equation is designed. • The refraction-based learning is introduced to enhance the diversity. • The extensive results show the competitiveness of the proposed algorithm. Throughout the last decade, high-dimensional function optimization problems have received substantial research interest in the field of intelligence computation. Butterfly optimization algorithm is a new meta-heuristic technique that has been proven to be more competitive than other optimization methods on low- dimensional problems. The basic butterfly optimization algorithm and its variants are not used to solve high-dimensional problems. Therefore, this paper develops an improved version of butterfly optimization algorithm to deal with the high- dimensional optimization problems. Inspired by particle swarm optimization, a modified position updated equation is designed by introducing velocity item and memory item in the local search phase to guide the search for candidate individuals. Moreover, a novel refraction-based learning strategy is introduced into butterfly optimization algorithm to effectively enhance diversity and exploration. The effectiveness of the proposed algorithm is tested on 40 benchmark problems with 100, 1000, 10,000 dimensions. The statistical results show that our algorithm has better performance than the basic butterfly optimization algorithm, its variants, and other population-based approaches to deal with the high-dimensional optimization problems. Finally, the high-dimensional feature selection and fault identification of wind turbine problems are solved and the comparisons show that the proposed algorithm outperforms better than most methods in terms of classification accuracy and number of the optimal feature subset.
Application of transfer learning and ensemble learning in image-level classification for breast histopathology

Background: Breast cancer has the highest prevalence among all cancers in women globally. The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern. In computer-aided diagnosis, most traditional classification models use a single network to extract features, although this approach has significant limitations. Moreover, many networks are trained and optimized on patient-level datasets, ignoring lower-level data labels. Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions. First, the BreaKHis dataset is randomly divided into training, validation, and test sets. Then, data augmentation techniques are used to balance the numbers of benign and malignant samples. Third, based on their transfer learning performance and the complementarity between networks, VGG16, Xception, ResNet50, and DenseNet201 are selected as base classifiers. Results: In a ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of 98.90%. To verify the capabilities of our method, it is experimentally compared with the latest Transformer and multilayer perception (MLP) models on the same dataset. Our ensemble model shows a a 5%–20% advantage, emphasizing its far-reaching abilities in classification tasks. Conclusion: This research focuses on improving the performance of a classification model with an ensemble algorithm. Transfer learning has an essential role in classification of small datasets, improving training speed and accuracy. Our model outperforms many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.