Reviewerly/exHarmony huggingface dataset:
Hardware and Software Solutions for the Generation of a Database of HSV-Color Characteristics for the Main Ores and Rocks of the Khibiny Massif

This article presents developed hardware and software solutions based on the application of machine vision technology. The hardware and software solutions were created in order to generate a database of HSV-color value for the main ore types, host rocks, and minerals to define criteria for the in-process identification of the Khibiny apatite in testing the walls of blasting boreholes. The hardware ensures a multi-parametric assessment of the optical characteristics of samples and minerals located on their surface. The designed software solution allows the user to control the measurement process; systematize a description of the textural and structural features of the sample under study; and process images of the core surface. The resultant database of HSV-color value for the main ores and rocks of the Khibiny massif and their constituent minerals will provide an opportunity to search for criteria for the in-process identification of the Khibiny apatite in a mineral mixture.
Advancing Human Motion Recognition with SkeletonCLIP++: Weighted Video Feature Integration and Enhanced Contrastive Sample Discrimination

This paper introduces 'SkeletonCLIP++', an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, 'Weighted Frame Integration' (WFI), shifts video feature computation from averaging to a weighted frame approach, enabling a more nuanced representation of human movements in line with semantic relevance. Another key development, 'Contrastive Sample Identification' (CSI), introduces a novel discriminative task within the model. This task involves identifying the most similar negative sample among positive ones, enhancing the model's ability to distinguish between closely related actions. Incorporating the 'BERT Text Encoder Integration' (BTEI) leverages the pre-trained BERT model as our text encoder to refine the model's performance. Empirical evaluations on HMDB-51, UCF-101, and NTU RGB+D 60 datasets illustrate positive improvements, especially in smaller datasets. 'SkeletonCLIP++' thus offers a refined approach to human action recognition, ensuring semantic integrity and detailed differentiation in video data analysis.
Enhanced YOLOv5s-Based Algorithm for Industrial Part Detection

In complex industrial environments, accurate recognition and localization of industrial targets are crucial. This study aims to improve the precision and accuracy of object detection in industrial scenarios by effectively fusing feature information at different scales and levels, and introducing edge detection head algorithms and attention mechanisms. We propose an improved YOLOv5-based algorithm for industrial object detection. Our improved algorithm incorporates the Crossing Bidirectional Feature Pyramid (CBiFPN), effectively addressing the information loss issue in multi-scale and multi-level feature fusion. Therefore, our method can enhance detection performance for objects of varying sizes. Concurrently, we have integrated the attention mechanism (C3_CA) into YOLOv5s to augment feature expression capabilities. Furthermore, we introduce the Edge Detection Head (EDH) method, which is adept at tackling detection challenges in scenes with occluded objects and cluttered backgrounds by merging edge information and amplifying it within the features. Experiments conducted on the modified ITODD dataset demonstrate that the original YOLOv5s algorithm achieves 82.11% and 60.98% on mAP@0.5 and mAP@0.5:0.95, respectively, with its precision and recall being 86.8% and 74.75%, respectively. The performance of the modified YOLOv5s algorithm on mAP@0.5 and mAP@0.5:0.95 has been improved by 1.23% and 1.44%, respectively, and the precision and recall have been enhanced by 3.68% and 1.06%, respectively. The results show that our method significantly boosts the accuracy and robustness of industrial target recognition and localization.
Experience in Measuring Productivity of Suffix Morphemes in Chinese Military News Discourse Based on a Universal Measure

The article studies the issues of measuring derivational productivity of affixes. It considers the concept of productivity and its various aspects which can be quantified by corpus linguistics methods. In particular, it analyzes the measures of “expanding” and “potential” productivity by H. Baayen, and proposes a new formula for “universal” productivity on the basis of the analysis. These three productivity measures are applied to 80 Chinese suffix morphemes (suffixes and semi-suffixes) within a thematic collection of texts – Chinese media reports on military topics for 2019. The language material comprises 2066 texts (2.852.262 characters). The study resulted in three variants of the rating list for the most productive suffix morphemes of the Chinese military news discourse, which can be used for didactic and applied purposes. H. Baayen’s “expanding productivity” and the “universal productivity” proposed in the work showed a high correlation (0.85), while H. Baayen’s “potential productivity” correlates with them negatively. A meaningful analysis of the results also brings up a question of the appropriateness of using the “potential productivity” measure. Comparison of existing measures and the formula proposed in the work allow us to conclude that for an approximate assessment of the suffix morphemes productivity one can use the formula of “expanding productivity” by H. Baayen, which, despite relying only on the single word token (hapax legomenon) frequency, gives quite plausible results. To measure the suffix morphemes productivity more precisely, it is recommended to use the “universal productivity” formula proposed in this paper which takes into account a larger number of significant parameters (the number of hapax legomena with the estimated affix, the total type count with the given affix, and the average regular word token frequency of the estimated affix) and is more reasonable in this respect.
Three-Dimensional Path Planning Based on Six-Direction Search Scheme

In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.
Applying Geostatistics to Understand Seismic Activity Patterns in the Northern Red Sea Boundary Zone

A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.
Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting

In this paper, we report the use of a superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, such as the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on a single-qubit device to fit the u-quark parton distribution function of the proton. In the final section of the manuscript, we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favoring applications like axion dark matter searches.
Hardware acceleration of DNA pattern matching using analog resistive CAMs

DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms of run time and power consumption. In this paper, we propose an efficient pipelined hardware accelerator that determines the chance of the occurrence of repeat-expansion diseases using DNA pattern matching. The proposed design parallelizes the DNA pattern matching task using associative memory realized with analog content-addressable memory and implements an algorithm that returns the maximum number of consecutive occurrences of a specific pattern within a DNA sequence. We fully implement all the required hardware circuits with PTM 45-nm technology, and we evaluate the proposed architecture on a practical human DNA dataset. The results show that our design is energy-efficient and accelerates the DNA pattern matching task by more than 100× compared to the approaches described in the literature.
Quantum neural network with privacy protection of input data and training parameters

Abstract It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for <?CDATA ${{\rm{R}}}_{{\rm{z}}},$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">z</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> </mml:math> <?CDATA ${{\rm{R}}}_{{\rm{x}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">x</mml:mi> </mml:mrow> </mml:msub> </mml:math> and <?CDATA ${{\rm{R}}}_{{\rm{y}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">y</mml:mi> </mml:mrow> </mml:msub> </mml:math> gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O (4n).
The Single-Photon Scattering Properties of Three-Level Giant Atoms under the Interaction of Dissipation and Local Coupling

The coupling of three-level giant atoms with one-dimensional waveguides can show interesting phenomena of transmission and reflection. Since the non-waveguide mode can cause the dissipation of external atoms, we consider the effect of the dissipation rate on the scattering of single photons in the system with giant atom–waveguide coupling. We find that as the dissipation rate of giant atoms increases, the transmission rate of a single photon increases and the reflection rate decreases. In addition, by varying the phase difference and decay rate, the giant atoms are able to achieve perfect transmission and total reflection over the entire frequency range. We also find and show the conditions for the conversion of the optimal frequency. When the cumulative phase of photons reaches a certain value, the system can achieve perfect transmission, which is independent of frequency. This model of coupling giant atoms with waveguides has a promising application in quantum communication and quantum information processing.
Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

At present, in the vast majority of coal mine production processes in China, the degree of hydrogeological exploration often lags behind geological exploration. The main difficulty in evaluating the water richness of coal seam top and bottom water-bearing beds is that the existing evaluation methods often rely on less hydrogeological investigation data. How to utilize the abundant geological exploration data in the mining area to appraise the water-rich distribution of sandstone aquifers is a feasible and challenging methodology. At present, some experts and scholars have tried to use multivariate factor analysis to solve the problem of water-richness evaluation, and they have achieved certain results, but there are some shortcomings: (1) The prediction results are mostly qualitative estimations of the water-richness grade, and there is a lack of quantitative analysis of the units-inflow; and (2) at present, the more advanced prediction methods, such as the back propagation (BP) neural network model, have the disadvantages of low accuracy, requiring many iterations, and slow convergence speed. Therefore, with geological exploration data of the 1503E working face of the Yili No.1 coal mine as the basis., this paper uses grey correlation analysis to screen out the factor indexes suitable for the evaluation of the water richness of a weakly cemented sandstone aquifer, and it combines principal component analysis (PCA) with a BP neural network. Based on the selected factor indexes, a prediction model of the water richness of a weakly cemented sandstone aquifer is established. The results show that compared with the existing methods, the prediction accuracy is higher and has a certain application value.
Pooled Steganalysis via Model Discrepancy

Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses on the discrepancy of the statistical characteristics of transmitted multiple images and designs a model-based effective pooled steganalysis strategy. Facing the public and monitored channel, without using the feature extractions, pooled steganalysis collects a set of images transmitted by a suspicious actor and use the corresponding distortion values as the statistic representation of the selected image set. Specifically, the normalized distortion of the suspicious image set generated via normal/guilty actor(s) is modelled as a normal distribution, and we apply maximum likelihood estimation (MLE) to estimate the parameter (cluster center) of the distribution by which we can represent the defined model. Considering the tremendous distortion difference between normal and stego image sets, we can deduce that the constructed model can effectively discover and reveal the existence of abnormal behavior of guilty actors. To show the discrepancy of different models, employing the logistic function and likelihood ratio test (LRT), we construct a new detector by which the ratio of cluster centers is turned into a probability. Depending on the generated probability and an optimal threshold, we make a judgment on whether the dubious actor is normal or guilty. Extensive experiments demonstrate that, compared to existing pooled steganalysis techniques, the proposed scheme exhibits great detection performance on the guilty actor(s) with lower complexity.
El léxico coloquial proveniente del lenguaje juvenil en la lengua de ficción española e italiana, versiones originales y meta

Abstract Youth lexicon has penetrated and become consolidated in the common colloquial register of adult speakers of any given language. This study uses a comparable bilingual corpus of original Spanish and Italian series, and a parallel trilingual corpus which includes the target Spanish and Italian versions of an analogous French series in order to investigate the use of such voices in national and foreign serial products of both cultures. The frequency of use per minute and the repertoire of terms inserted in the series of the two countries have been compared. The results show that the number of occurrences per minute in the Spanish original fictional language is much higher than in the Italian one, where this kind of words and expressions are often excluded. These data coincide with the number of occurrences per minute recorded in the Spanish and Italian target versions. Therefore, the parallelism between the original and target products of both languages shows that hybridization and domestication have been the predominant strategies in the translation for dubbing.
A Survey of Ethereum Smart Contract Security: Attacks and Detection

A smart contract is a computerised transaction agreement that carries out predefined terms without human involvement or third-party intermediaries. It serves as a trust intermediary in several industries, including finance, insurance, and supply chain management, in the blockchain 2.0 era. With the increasing interest in smart contracts, security has become a serious problem. Examining typical vulnerability types and vulnerability detection methodologies is of special importance. In this research, a comprehensive evaluation of common smart contract security vulnerabilities is conducted, and a three-tier threat model is then provided to classify the vulnerabilities. In addition, we examine fourteen existing smart contract analysis tools for finding vulnerabilities and classify them according to the main technique they apply. This paper is designed to serve as a reference for people who wish to analyse deployed code and enhance existing detection techniques. At the conclusion, open issues and future research paths regarding smart contract vulnerability detection are presented.
Good models borrow, great models steal: intellectual property rights and generative AI

Abstract Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.
Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.