Reviewerly/exHarmony huggingface dataset:
Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the Sahlabad area, South Khorasan province, East Iran. In this investigation, 709 stream sediment samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and geostatistical and machine learning techniques. Subsequently, hierarchical analysis (HA), Spearman’s rank correlation coefficient, concentration–area (C–A) fractal analysis, Kriging interpolation, and descriptive statistics studies were performed on the geochemical dataset. Machine learning algorithms, namely K-means clustering, factor analysis (FA), and linear discriminant analysis (LDA) were employed to deliver a comprehensive geochemical model of copper mineralization in the study area. The identification of trace elements and the predictor composition of copper mineralization, the separation of copper geochemical communities, and the investigation of the geochemical behavior of copper vs. its trace elements were targeted and accomplished. As a result, the elements Ag, Mo, Pb, Zn, and Sn were distinguished as trace elements and predictors of copper geochemical modeling in the study area. Additionally, geochemical anomalies of copper mineralization were identified based on trace elements. Conclusively, the nonlinear behavior of the copper element versus its trace elements was modeled. This study demonstrates that the integration and synchronous use of geostatistical and machine learning methods can specifically deliver a comprehensive geochemical modeling of ore mineralization for prospecting mineral anomalies in metallogenic provinces around the globe.
Aerial Gaze Target Recognition Based on Head and Eye Movements

Aerial gaze target recognition is an important step in aerial eye control interaction. In order to achieve accurate aerial gaze target recognition, the VT4LM recognition algorithm was constructed in this paper. By this algorithm, the facial images containing the eyes of the flight operators were first inputted into Vision Transformer (ViT) to extract the local features, and the head posture of the flight operators was inputted into four LSTMs to extract the global features. Then, the local and global features of the flight operators were inputted into three fully connected layers, two dropout layers and one softmax classifier. Finally, the recognition result of aerial gaze target was obtained. In this paper the effectiveness of the VT4LM recognition algorithm was verified through the identification of four aerial line-of-sight gaze targets named Head-up display, Accelerator push rod, Control lever and Rudder by four flight operators during simulated flight. The experimental results showed that the accuracy of the VT4LM algorithm for aerial gaze target recognition reached 89.29% and the Cross Entropy Loss was 1.45. Compared to the other three recognition methods, the VT4LM algorithm had the highest recognition accuracy and minimum loss. When using the VT4LM algorithm to detect four simulated flight operators staring at four aerial gaze targets, the recognition accuracy was all higher than 85.00%. It could be seen that the VT4LM algorithm had a good performance in aerial gaze target recognition.
Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

In this study, we present a machine-learning-based approach that focuses on the automatic retrieval of engraved expiry dates. We leverage generative adversarial networks by augmenting the dataset to enhance the classifier performance and propose a suitable convolutional neural network (CNN) model for this dataset referred to herein as the CNN for engraved digit (CNN-ED) model. Our evaluation encompasses a diverse range of supervised classifiers, including classic and deep learning models. Our proposed CNN-ED model remarkably achieves an exceptional accuracy, reaching a 99.88% peak with perfect precision for all digits. Our new model outperforms other CNN-based models in accuracy and precision. This work offers valuable insights into engraved digit recognition and provides potential implications for designing more accurate and efficient recognition models in various applications.
A primer on the insurability of decentralized finance (DeFi)

Abstract Decentralized finance (DeFi), a blockchain-based form of alternative financial markets, has gained significant public attention in recent months. Despite its relatively short history, DeFi offers a range of opportunities for designing and transferring digital assets. This establishes market structures that bear resemblance to traditional financial markets. Notably, the landscape of DeFi projects has expanded to include insurance protocols that offer DeFi-inherent mechanisms for hedging DeFi-specific risks, particularly those associated with smart contracts. These insurance protocols aim to provide similar value propositions as traditional insurance, namely the minimization and transfer of risks in exchange for a premium. However, it is crucial to acknowledge that most of these risk transfer protocols are strongly dependent on subjective expectations and decentralized governance structures. This article aims to develop a taxonomical understanding of DeFi insurance. Moreover, it seeks to assess the insurability of risks related to smart contracts. By doing so, this study contributes to the emerging body of knowledge surrounding DeFi insurance, paving the way for further research and analysis in this evolving field.
Securing Financial Transactions with a Robust Algorithm: Preventing Double-Spending Attacks

A zero-confirmation transaction is a transaction that has not yet been confirmed on the blockchain and is not yet part of the blockchain. The network propagates zero-confirmation transactions quickly, but they are not secured against double-spending attacks. In this study, the proposed method is used to secure zero-confirmation transactions by using the security hashing algorithm 512 in elliptic curve cryptography (ECDSA) instead of the security hashing algorithm 256. This is to generate a cryptographic identity to secure the transactions in zero-confirmation transactions instead of security hashing algorithm 256. The results show that SHA-512 is greater than SHA-256 in throughput. Additionally, SHA-512 offers better throughput performance than SHA-256 while also having a larger hash size. Results also show that SHA-512 is more secure than SHA-256.
Research on Text Emotion Analysis Based on BMCBMA Model in Online Education Environment

This paper suggests a text emotion analysis approach built on the BMCBMA model for use in an online education environment aiming at the issues of poor text utilization, challenge in effective information extraction, and a failure to effectively recognize word polysemy in existing text emotion analysis task research. Initially, a language model is constructed using Bidirectional Encoder Representations from Transformers (BERT) pre-training. The word vector trained by BERT pre-training is selected to supplant the word vector trained in the conventional manner, and the semantic vector is generated dynamically using the context of the word. Second, the Bi-directional Long Short-Term Memory (BiLSTM) model is employed to record semantic information by simultaneously integrating both positive and negative orientations to assess the emotional polarity information for every text in its entirety. The Multi-channel Convolutional Neural Networks (CNN) model is then utilized to derive textual local feature information of text. Lastly, the attention mechanism is used to cause all types of features to interact and to derive a deeper semantic association within the context. Experiments indicate that the suggested method has accuracy, F1, recall, and precision values of 0.915, 0.911, 0.915 and 0.908, correspondingly. The suggested approach performs significantly better in text emotion analysis than the comparison method.
Neural Machine Translation of Electrical Engineering Based on Integrated Convolutional Neural Networks

Research has shown that neural machine translation performs poorly on low-resource and specific domain parallel corpora. In this paper, we focus on the problem of neural machine translation in the field of electrical engineering. To address the mistranslation caused by the Transformer model’s limited ability to extract feature information from certain sentences, we propose two new models that integrate a convolutional neural network as a feature extraction layer into the Transformer model. The feature information extracted by the CNN is fused separately in the source-side and target-side models, which enhances the Transformer model’s ability to extract feature information, optimizes model performance, and improves translation quality. On the dataset of the field of electrical engineering, the proposed source-side and target-side models improved BLEU scores by 1.63 and 1.12 percentage points, respectively, compared to the baseline model. In addition, the two models proposed in this paper can learn rich semantic knowledge without relying on auxiliary knowledge such as part-of-speech tagging and named entity recognition, which saves a certain amount of human resources and time costs.
Lightweight Image Denoising Network for Multimedia Teaching System

Due to COVID-19, online education has become an important tool for teachers to teach students. Also, teachers depend on a multimedia teaching system (platform) to finish online education. However, interacted images from a multimedia teaching system may suffer from noise. To address this issue, we propose a lightweight image denoising network (LIDNet) for multimedia teaching systems. A parallel network can be used to mine complementary information. To achieve an adaptive CNN, an omni-dimensional dynamic convolution fused into an upper network can automatically adjust parameters to achieve a robust CNN, according to different input noisy images. That also enlarges the difference in network architecture, which can improve the denoising effect. To refine obtained structural information, a serial network is set behind a parallel network. To extract more salient information, an adaptively parametric rectifier linear unit composed of an attention mechanism and a ReLU is used into LIDNet. Experiments show that our proposed method is effective in image denoising, which can also provide assistance for multimedia teaching systems.
Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index.
Graph Convolutional Network Design for Node Classification Accuracy Improvement

Graph convolutional networks (GCNs) provide an advantage in node classification tasks for graph-related data structures. In this paper, we propose a GCN model for enhancing the performance of node classification tasks. We design a GCN layer by updating the aggregation function using an updated value of the weight coefficient. The adjacency matrix of the input graph and the identity matrix are used to calculate the aggregation function. To validate the proposed model, we performed extensive experimental studies with seven publicly available datasets. The proposed GCN layer achieves comparable results with the state-of-the-art methods. With one single layer, the proposed approach can achieve superior results.
Multiplier Design for the Modulo Set $$\left\{ {2^{n} - 1,2^{n} ,2^{n + 1} - 1} \right\}$$ and Its Application in DCT for HEVC

The Residue Number System (RNS) is a non-weighted number system. Because of its inherent parallelism, it has been extensively studied and used in Digital Signal Processing (DSP) systems. A key arithmetic operation in residue-based real-time computing system is modulo multiplication. For small moduli, ROM-based structures are better at realizing multipliers. Implementations with arithmetic components are more for medium and large moduli due to the exponential growth of ROM sizes. The new modular multiplier introduced in this paper is capable of easily handling medium and large moduli. The multiplier unit is proposed in this paper using shift and add, followed by the modulo operation. The implementation results show that our proposed design outperforms existing architectures in terms of area and power consumption when using the TSMC-180 nm CMOS Technology. When compared to existing works, our proposed multiplier saves 86% to 93% of area and 65% to 83% of power. The proposed multiplier improves the existing DCT architecture by 18% in terms of area.
Detecting and Isolating Adversarial Attacks Using Characteristics of the Surrogate Model Framework

The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the purpose of detecting malicious data alterations. The core of the framework is based on building machine learning classifiers for the detection of attacks and its type that operate on diagnostic attributes. These diagnostic attributes are obtained not from the original model, but from the surrogate model that has been created by observation of the original model inputs and outputs. The paper presents building blocks for the framework and tests its power for the detection and isolation of attacks in selected scenarios utilizing known attacks and public machine learning data sets. The obtained results pave the road for further experiments and the goal of developing classifiers that can be integrated into real-world scenarios, bolstering the robustness of machine learning applications.
On the Two Abstractions of Social Information and Plato’s Theory of the Separation of Particulars and Universals

The authors propose that the theoretical framework of Western philosophy is supported by metaphysical ontology through the differentiation and analysis of two different kinds of “particulars-universals” in Plato’s “world visible to the naked eye” and “world known to the soul” in the solar metaphor and line metaphor. In today’s society, where big data, the Internet, and artificial intelligence are prevalent, we should say goodbye to Aristotle’s “physicalism”, return to Plato’s theory of ideas, and use theoretical information science to reinterpret the theory of ideas, change from the scientific paradigm of physicalism to the scientific paradigm of “informatics”, and consciously use the world view, scientific outlook, and methodology of information science to guide our study, life, and work.
Digital competences of teachers in the use of YouTube as an educational resource: analysis by educational stage and gender

Abstract The purpose of this study was to analyze the digital competence of teachers on the use of YouTube as a didactic resource in the teaching and learning process of students. Specifically, this was analyzed and compared for each educational stage (Preschool Education, Primary Education, Secondary Education, Adult Education, and Vocational Education-VET) between genders of the teacher (female and male). For this, an ex post facto non-experimental design was used, with a sample of 2157 in-service teachers from all over Spain. Among the main results found in this study, teachers of all educational stages had satisfactory skills on the use of YouTube to search and communicate information, although low in relation to the creation of audio-visual material through YouTube. In addition, it was evidenced how gender was a significant factor which affected the competencies of teachers, in most of the educational stages analyzed.
Two Informatics Revolutions Promote the Leap of Science View and Methodology—On the Zeitgeist Embodied in Religious Psychology, Physical Psychology, and Information Psychology

The authors of this article believe that from the perspective of the development of human spiritual products, the transformation of our scientific worldview and methodology from the paradigm of physics to the paradigm of informatics is an inevitable law of historical development. This article introduces the concepts and theories of two information science revolutions. The first information science revolution transformed “information concepts” into “information tools”. The second information science revolution transformed “information tools” into “information concepts”, which had a decisive impact on the transformation of human scientific views and methodologies (classed as scientific paradigms). At the same time, we observe the development trajectory of “psychology”, which began from the original version of ancient Greece, which passed through “religious psychology” and “physical psychology” and developed into the currently known “information psychology”. We can see the evolution of the “era spirit”: from “gods” and “materials”, it inevitably moves towards “information”. Therefore, promoting the spirit of the times of “Informationism” is a common task for contemporary scientists and philosophers.
On the Physics Paradigm of Communication Engineering and the Informatics Paradigm of Dissemination Science

The authors of this article advocate the view that the transformation from a physics paradigm to an informatics paradigm is not a denial of the effectiveness and scope of application of the physics paradigm, but a criticism of the “arrogance” of the physics paradigm, which misleads research in the humanities and social sciences. Through the “disciplinary informatization” of the humanities and social sciences, their own disciplinary paradigms can be established. The research objects of “communication” and “dissemination”, respectively, are “material” and “information”, meaning that they should follow different scientific paradigms. After achieving the transformation into an informatics paradigm, we should strive to explore the “cooperation” between the two paradigms, allowing them to explain “nature” (such as biological gene DNA) and “society” (cultural gene MEME), especially human “thinking”. Obviously, the conditions for solving “physical and mental problems” since Plato and Descartes have already been met.
Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder

Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.
Use of Ensemble Learning to Improve Performance of Known Convolutional Neural Networks for Mammography Classification

Convolutional neural networks and deep learning models represent the gold standard in medical image classification. Their innovative architectures have led to notable breakthroughs in image classification and feature extraction performance. However, these advancements often remain underutilized in the medical imaging field due to the scarcity of sufficient labeled data which are needed to leverage these new features fully. While many methodologies exhibit stellar performance on benchmark data sets like DDSM or Minimias, their efficacy drastically decreases when applied to real-world data sets. This study aims to develop a tool to streamline mammogram classification that maintains high reliability across different data sources. We use images from the DDSM data set and a proprietary data set, YERAL, which comprises 943 mammograms from Mexican patients. We evaluate the performance of ensemble learning algorithms combined with prevalent deep learning models such as Alexnet, VGG-16, and Inception. The computational results demonstrate the effectiveness of the proposed methodology, with models achieving 82% accuracy without overtaxing our hardware capabilities, and they also highlight the efficiency of ensemble algorithms in enhancing accuracy across all test cases.
Handwriting-Based Text Line Segmentation from Malayalam Documents

Optical character recognition systems for Malayalam handwritten documents have become an open research area. A major hindrance in this research is the unavailability of a benchmark database. Therefore, a new database of 402 Malayalam handwritten document images and ground truth images of 7535 text lines is developed for the implementation of the proposed technique. This paper proposes a technique for the extraction of text lines from handwritten documents in the Malayalam language, specifically based on the handwriting of the writer. Text lines are extracted based on horizontal and vertical projection values, the size of the handwritten characters, the height of the text lines and the curved nature of the Malayalam alphabet. The proposed technique is able to overcome incorrect segmentation due to the presence of characters written with spaces above or below other characters and the overlapping of lines because of ascenders and descenders. The performance of the proposed method for text line extraction is quantitatively evaluated using the MatchScore value metric and is found to be 85.507%. The recognition accuracy, detection rate and F-measure of the proposed method are found to be 99.39%, 85.5% and 91.92%, respectively. It is experimentally verified that the proposed method outperforms some of the existing language-independent text line extraction algorithms.