Reviewerly/exHarmony huggingface dataset:
Lightweight Computational Complexity Stepping up the NTRU Post-Quantum Algorithm Using Parallel Computing

The Nth-degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU to be the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations on large vectors, which makes parallel computing one of the appropriate solutions to speed it up. NTRUEncrypt operations contain a large amount of data that requires many repetitive arithmetic operations. These operations make it a strong candidate to take advantage of the high degree of parallelism. The main costly operation that is repeated in all NTRU algorithm steps is polynomial multiplication. In this work, a Parallel Post-Quantum NTRUEncrypt algorithm called PPQNTRUEncrypt is proposed. This algorithm exploits the capabilities of parallel computing to accelerate the NTRUEncrypt algorithm. Both analytical and Apache Spark simulation models are used. The proposed algorithm enhanced the NTRUEncrypt algorithm by approximately 49.5%, 74.5%, 87.6%, 92.5%, 93.4%, and 94.5%, assuming that the number of processing elements is 2, 4, 8, 12, 16, and 20 respectively.
Identification of Hydrochemical Characteristics, Spatial Evolution, and Driving Forces of River Water in Jinjiang Watershed, China

Rivers are an important source of water in humid regions, but their availability is greatly limited by water chemistry. In order to accurately identify the changes in river water chemical composition, the compositional analysis method (CoDA) is proposed from the perspective of compositional data analysis theory, which considers the geochemical riverine system as a whole and detects the compositional changes of the entire watershed. The basic data analysis is carried out by traditional analysis methods, and the results show that the hydrochemical characteristics of different sections of the basin have significant features. The water chemistry of Dongxi River is of the HCO3− Ca type. The water of the Xixi River shows a gradual evolution from the HCO3-Ca type and high SO42− content in the upper reaches to the Cl-Ca type in the lower reaches. The hydrochemistry of river water in the watershed is mainly affected by rock weathering leaching (PC1) and agricultural and domestic pollutant discharge (PC2), with a contribution rate of 48.4% and 19.7%, respectively. Rock weathering, mining, and agricultural pollution are the main factors affecting the chemical composition of river water in different regions. The spatial composition of a single sample at different scales is monitored by the Mahalanobis distance approach in an iterative manner to minimize the influence of a single anomaly on the composition center. The results show that the main reasons for the change in river water chemical composition along the Xixi River are attributed to mine pollution, domestic pollution, and tea plantation and that along the Dongxi River is caused by domestic pollution. The hydrochemical composition changes after the confluence of the Xixi River and Dongxi River are mainly affected by human activities and seawater in urban areas. This research could provide new perspectives and methods for detecting the influences of human and natural factors on the hydrochemistry of river water in humid regions worldwide.
Radiometric Signatures of Gold Mineralization Zone in Pongkor, West Java, Indonesia: A Baseline for Radiometric Mapping Application on Low-Sulfidation Epithermal Deposit

Radiometric mapping could play a prominent role in locating the host rock or alteration that leads to gold mineralization. Nevertheless, in low-sulfidation epithermal gold deposits, the radiometric signatures have to be priorly characterized due to their geometry. It is comprised of a small ore vein system within the large alteration zones. The Pongkor gold mine is a low-sulfidation epithermal deposit and was selected for this purpose. The method started with the surface identification of radiometric signatures on altered and unaltered rocks near Pongkor using portable spectrometers. They are followed by the characterization of the underground mining front, which is comprised of different types of veins and host rocks. The results show that the altered rocks were characterized by a high K% and a low eTh/K ratio. Vice versa, the mineralized veins show low radioelement concentrations. Following the characterization of the geometry of alteration zones and mineralized veins, a study of the relationship between radioelements detected by radiometric mapping and gold pathfinder elements was conducted. Gold pathfinders of Mn, Fe, Zn, As, and Pb were selected for correlation studies with the radioelement. The pathfinders and radioelements were more significantly correlated in veins compared to the host rock. Based on this study, radiometric mapping has the potential and benefit of being applied in the exploration of low-sulfidation epithermal gold deposits. An alteration zone could be delineated by K or eTh/K as an anomaly indicator, and the vein bodies could also be delineated using low K or eTh as an anomaly indicator.
CoDA-Based Geo-Electrochemical Prospecting Prediction of Uranium Orebodies in Changjiang Area, Guangdong Province, China

In order to achieve a breakthrough in the exploration of uranium orebodies in the Changjiang area of Guangdong province in China, the geo-electrochemical exploration method is used for mineral resource prediction. The logarithmically and isometric log-ratio (ilr) transformations are applied to the geo-electrochemical data in this study area to extract geochemical anomalies. The relationship between element associations and mineralization is revealed through descriptive statistical analysis and further biplot analysis. Then, the energy spectrum density–area fractal model (S-A model) is used to identify geochemical backgrounds and anomalies. The results show that: (i) the logarithmically and ilr-transformed data are more uniform, and they more or less obey the rules of normal distribution; (ii) the biplot shows that the ilr-transformed data eliminates the closure effect, and the robust principal component analysis (RPCA) has a better indicative significance for element associations—PC1 reveals the mineralization element association dominated by U and the multiple periods of hydrothermal activity; (iii) the S-A method could extract the local anomalies from different geochemical backgrounds, which indicates mineralization is more reliable. Finally, four favorable prospecting targets are delineated based on the geological and geochemical indicators.
Multi-Label Weighted Contrastive Cross-Modal Hashing

Due to the low storage cost and high computation efficiency of hashing, cross-modal hashing has been attracting widespread attention in recent years. In this paper, we investigate how supervised cross-modal hashing (CMH) benefits from multi-label and contrastive learning (CL) by overcoming the following two challenges: (i) how to combine multi-label and supervised contrastive learning to consider diverse relationships among cross-modal instances, and (ii) how to reduce the sparsity of multi-label representation so as to improve the similarity measurement accuracy. To this end, we propose a novel cross-modal hashing framework, dubbed Multi-Label Weighted Contrastive Hashing (MLWCH). This framework involves compact consistent similarity representation, a new designed multi-label similarity calculation method that efficiently reduces the sparsity of multi-label by reducing redundant zero elements. Furthermore, a novel multi-label weighted contrastive learning strategy is developed to significantly improve hashing learning by assigning similarity weight to positive samples under both linear and non-linear similarities. Extensive experiments and ablation analysis over three benchmark datasets validate the superiority of our MLWCH method, especially over several outstanding baselines.
DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images.
Ways to Obtain the Consent of the Subject of Personal Data for Their Processing in the Process of Organizing Educational Online Courses

The authors, using an example from their teaching practice, consider a special case of obtaining and processing the consent of the subject of personal data to the processing of personal data. The relationship between an educational organization and students of online advanced training courses is taken as an example. The authors show the problems they faced as organizers of educational activities and share their experience in solving these problems. They come to the conclusion that consent to the processing of personal data can be obtained using such electronic means of communication and datatransmission as e-mail, the Yandex Forms service and similar services. The use of these electronic means of communication allows you to quickly and economically solve some organizational issues that arise in educational practice.
Exploring graph representation strategies for text classification

Since 2005, the deep learning community has had access to input graphs to their models. So, the natural language processing (NLP) community started using this technique to process text. However, a challenge that the graph neural networks (GNN) may encounter is the sensibility to representation format. Since different graphs can represent the same text, the model’s performance may change depending on the representation used. Even though many practitioners have this intuition, only some works touch on this aspect of GNN. Therefore, we explore twelve different text representation strategies that build graphs from text and apply them to the same GNN to investigate how different graphs may affect the results. We divide these strategies into four groups: reading order, dependency-based, binary tree, and graph of words. From these groups, we created the binary tree group for this paper. Nevertheless, in our tests, we observed that the dependency-based representations tend to achieve better performance: The dependency-based methods allow us to stay competitive in five relevant datasets and beat the state-of-the-art in another dataset. These results suggest that performing representation tuning can be a valuable technique to improve a deep learning model.
Entropic taming of the Look Elsewhere Effect

Abstract To mitigate the Look Elsewhere Effect in multiple hypothesis testing using p -values, the paper suggests an “entropic correction” of the significance level at which the null hypothesis is rejected. The proposed correction uses the entropic uncertainty associated with the probability measure that expresses the prior-to-test probabilities expressing how likely the confirming evidence may occur at values of the parameter. When the prior-to-test probability is uniform (embodying maximal uncertainty) the entropic correction coincides with the Bonferroni correction. When the prior-to-test probability embodies maximal certainty (is concentrated on a single value of the parameter at which the evidence is obtained), the entropic correction overrides the Look Elsewhere Effect completely by not requiring any correction of significance. The intermediate situation is illustrated by a simple hypothetical example. Interpreting the prior-to-test probability subjectively allows a Bayesian spirit enter the frequentist multiple hypothesis testing in a disciplined manner. If the prior-to-test probability is determined objectively, the entropic correction makes possible to take into account in a technically explicit way the background theoretical knowledge relevant for the test.
<i>K</i>th min Threshold Encryption for Privacy-preserving Data Evaluation

Abstract The $k$th min threshold is to judge whether the $k$th smallest element of an attribute set along with a confidential file is greater than a predefined threshold, which is a fundamental, primitive operation in data evaluation, such as risk evaluation in business investment. However, it will compromise the privacy of the confidential files when proceeding with such a data evaluation because there is often a large amount of sensitive information involved in them, which the organizations/individuals are reluctant to expose due to the risk of losing a competitive advantage. Motivated by the issue how to preserve the privacy of the confidential files during data evaluation, in this research, we first present a new encryption notion called $k$th min threshold encryption (KTE) for serving privacy-preserving data evaluation. In this notion, the confidential file will be encrypted under an attribute set for its privacy protection prior to being sent to a receiver, and a decryption key is generated from a threshold $d$ and a rank $k$ of element, both selected by the receiver. The decryption will be successful if and only if the $k$th smallest element of the attribute set is greater than $d$. We then describe a concrete construction of KTE in the public-key setting. In particular, our construction features optimally short private keys, which only consists of one group element. By virtue of this advantage, it is quite practical because of only two pairing operations for decryption computation.
Visual-inertia system with dynamic line feature extraction algorithm

In response to the problem of decreased robustness and accuracy in visual inertial systems due to the inability to extract sufficient point features in weak-textured environments, a dynamic line feature algorithm is proposed, adding the number of co-visible point features as a dynamic threshold to adjust the line feature extraction process. Line features are easier to track in weak-textured environments, providing more geometric information and improving system robustness. The backend utilizes a sliding window algorithm to establish an objective function and jointly optimize the visual-inertial information to improve system accuracy. Experimental results using publicly available datasets demonstrate that compared to the VINS-Mono system, the algorithm exhibits better accuracy and robustness under weak-textured environments.
Research on multi-level network intelligence collection and analysis practice

Under the current big data background, network crimes are happening frequently, and the network intelligence collection and analysis technology is particularly important for the governance of the crimes. To deal with the issue, we propose a new multi-level network intelligence collection and analysis practice approach, so as to improve the network intelligence collection and analysis ability of network security talents under the new situation, and adapt to the requirements of new network crime governance. First, we introduce the whole design of the network intelligence collection and analysis course, then elaborate on the details of the new multi-level mode, and finally analyze and summarize the preliminary exploration of the practice. The application results show that the multi-level practice approach is better than the common method, which is helpful to strengthen the participants' practical ability of network intelligence collection and analysis.
Algorithm for urban air mobility object recognition based on spiking neural networks

In recent years, a large number of intelligent unmanned aerial vehicles (UAVs) have emerged in people's vision, leading to the development of a new safe and efficient urban air mobility (UAM) system, which is suitable for both manned and cargo scenarios. Driven by the market, this service is expected to become a new trend in the development of civil aviation. The rapid development of deep learning has brought prospects to urban air traffic, but the increasing data volume and model complexity pose challenges in terms of resource consumption and model deployment. A method of applying spiking neural network (SNN) algorithms in UAM is proposed in this paper, utilizing spike-based transmission to significantly reduce energy consumption compared to deep learning algorithms. To address the issue of missing urban air traffic data sets, we build a complex urban air traffic data set comprising 10 categories such as pedestrians, vehicles, buildings, traffic signals, billboards, garbage bins, unmanned aerial vehicles, hot air balloons, cats, and dogs. In the network model, we introduce the biological neurons such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF), achieving the highest accuracy of 81.937% and 82.772%, respectively. Based on the LIF neuron, we propose the Self-Learning Leaky Integrate-and-Fire (SLLIF) neuron, which autonomously learns stimulus-input ratio relationships to better align with the brain's automatic optimization mechanism, achieving a recognition accuracy of 85.484%. Furthermore, we re-evaluate the selection of the hyper parameter time steps and choose suitable values for different neural network models to reduce resource consumption while maximizing accuracy.
Focused crawler based on concept context graph

In order to improve the performance of focused crawler, a topic crawling strategy based on Concept Context Graph(CCG) is proposed. User knowledge background is constructed from the initial topic text returned by zhishi.me Knowledge Graph, and it is transformed into a CCG to guide the crawling of focused crawler. During the crawling process, the crawler treats the hyperlinks it encounters as a Virtual Concept(VC), and then calculates the Sememe Similarity(SS) between the VC and the core concept in CCG based on HowNet. According to the relationship between the SS value and the given threshold, it is decided whether to keep the link or not, and the CCG is updated for the reserved VC. The corresponding hyperlinks of the preserved VC are then stored in the queue to be crawled according to the order of SS value from high to low. Through experiments, it can be seen that the topic crawling strategy can improve the performance of focused crawler.
Research on computational fluid dynamics literature mining methods using natural language processing

In our paper, we propose a methodology that uses natural language processing technology, including the Latent Dirichlet Allocation model and Word2Vec word embedding model, along with various machine learning and deep learning algorithms. This approach allows us to analyze Computational Fluid Dynamics literature accurately and efficiently. By applying these techniques to extract topic and word relationships from the literature data, we can discover hotspots, research directions, and emerging trends in the field. Our results validate the effectiveness of this approach, providing valuable insights. This research also offers a framework for researchers to analyze scientific literature, helping them understand development trends and explore knowledge associations in their respective fields.
Network resource analysis and evaluation research oriented to task requirements

Aiming at the communication requirements of distributed mission management of aviation systems, this paper takes the mission requirements as input and improves them based on the ADC model under the condition of flexible mission conditions and dynamic changes in cluster networking network topology. In this paper, the analytic hierarchy method (AHP) in systems science is used to analyze the performance of communication networks hierarchically, and the fuzzy comprehensive evaluation method (FCE) is used as a multi-index evaluation method. From the five aspects of system performance definition: responsiveness, processing ability, effectiveness, resource capacity and robustness, the comprehensive performance of the network in complex environment is evaluated in multiple dimensions.
Better image dehazing networks based on structural priors

Convolutional neural networks are more and more widely used in our lives. Computer vision tasks dominated by traditional algorithms are also trying to introduce deep learning networks. Image dehazing is a good field. At present, the most widely used image dehazing field is the unsupervised dehazing algorithm. By decomposing low-quality images, image dehazing (separation into clear image and haze map), it can be performed expansion operations. At present, due to the lack of paired high-quality images and hazy images in the field of image dehazing, this further intensifies the need for unsupervised dehazing algorithms. In order to further improve this problem, this article proposes an extractor DIPV1 based on a new U-Net-like structure, and based on this, combined with the biased fitting problem of the network, the existing Double-DIP structure is improved. we call it Better Double-DIP(BDouble-DIP). This network is an end-to-end network and is jointly trained by 2 DIP-V1.By stopping early, we can get better results. By applying the physical dehazing model, we can finally get a good dehazing result. We conduct experiments on the open source dataset O-Haze dataset, and compare BDoubleDIP with existing traditional algorithms: SIHR, EIDBC, supervised neural networks like DehazeNet: NTDF, NLD, URIE, and BOPBL, as well as unsupervised neural networks: DoubleDIP
Combining image brightness enhancement algorithm with YOLOv5 for nest detection in power transmission lines

With the development of urban construction, the power infrastructure continues to expand and improve. Power transmission lines, in particular, are numerous and widely distributed. The maintenance, inspection, and troubleshooting of these lines are essential for the stable operation of the power grid. However, the proliferation of birds has expanded their activity range, posing a significant threat to the power system. Therefore, this paper combines an image brightness enhancement algorithm with YOLOv5 to effectively detect nest images under low-light conditions. Experimental results have demonstrated the effectiveness of the proposed algorithm in this paper.