Reviewerly/exHarmony huggingface dataset:
Interaction of Stress-Strain State of the Reinforced Soil Mass and Vertical Shaft Mouth Lining with the Mine Surface Building’s Foundations

Introduction . The mining of mineral deposits is related to the construction of sophisticated technological complexes on the surface being a liaison between underground operations and above-ground systems. During construction of the mining companies’ buildings and structures of the above-ground technological complexes there arise problems related to the complicated engineering, geological and hydrogeological conditions, as well as emerge difficulties caused by the presence of underground workings (mining, transport, ventilation, auxiliary) affecting the construction and operation of surface facilities. In this article a study was made of the stress-strain state (SSS) of a complex system comprising a mine surface building, a vertical shaft mouth with a ventilation duct under conditions complicated by the presence of subsiding soils and the need of reinforcement thereof. Materials and methods. In the study there were used: the analysis of existing methods of soil bases reinforcement as applied to the specific engineering and geological conditions of construction, subject to the interdependence of aboveground and underground structures; the mathematical modeling of the system "a vertical shaft mouth with a ventilation duct - a subsiding soil mass reinforced with soil piles - a mine surface building’s foundations"; the study of SSS features on models, determination of the main factors affecting the design decisions on enforcement of soil bases and building foundations receiving a compound combination of operational loads. Results. It has been found that the above-ground loadbearing structures, foundations and soil bases should be calculated taking into account the interdependence with the vertical shaft and the adjacent ventilation duct, based on the SSS parameters definition. The monolithic concrete mouth lining has a significant impact on the SSS of the foundation slab, increasing rigidity at the points of contact between the slab and the lining. The minimum vertical settlement of the slab is observed above the shaft mouth lining, the maximum - in the central part of the building, in the points of maximum load application. Discussion and Conclusions. The results of the stress-strain state study of a reinforced soil mass and a vertical shaft mouth lining in interaction with the mine surface building’s foundations are formulated, and recommendations are given on considering the affecting factors when designing such facilities.
Graph-Based Self-Training for Semi-Supervised Deep Similarity Learning

Semi-supervised learning is a learning pattern that can utilize labeled data and unlabeled data to train deep neural networks. In semi-supervised learning methods, self-training-based methods do not depend on a data augmentation strategy and have better generalization ability. However, their performance is limited by the accuracy of predicted pseudo-labels. In this paper, we propose to reduce the noise in the pseudo-labels from two aspects: the accuracy of predictions and the confidence of the predictions. For the first aspect, we propose a similarity graph structure learning (SGSL) model that considers the correlation between unlabeled and labeled samples, which facilitates the learning of more discriminative features and, thus, obtains more accurate predictions. For the second aspect, we propose an uncertainty-based graph convolutional network (UGCN), which can aggregate similar features based on the learned graph structure in the training phase, making the features more discriminative. It can also output the uncertainty of predictions in the pseudo-label generation phase, generating pseudo-labels only for unlabeled samples with low uncertainty; thus, reducing the noise in the pseudo-labels. Further, a positive and negative self-training framework is proposed, which combines the proposed SGSL model and UGCN into the self-training framework for end-to-end training. In addition, in order to introduce more supervised signals in the self-training process, negative pseudo-labels are generated for unlabeled samples with low prediction confidence, and then the positive and negative pseudo-labeled samples are trained together with a small number of labeled samples to improve the performance of semi-supervised learning. The code is available upon request.
Self-supervised zero-shot dehazing network based on dark channel prior

Most learning-based methods previously used in image dehazing employ a supervised learning strategy, which is time-consuming and requires a large-scale dataset. However, large-scale datasets are difficult to obtain. Here, we propose a self-supervised zero-shot dehazing network (SZDNet) based on dark channel prior, which uses a hazy image generated from the output dehazed image as a pseudo-label to supervise the optimization process of the network. Additionally, we use a novel multichannel quad-tree algorithm to estimate atmospheric light values, which is more accurate than previous methods. Furthermore, the sum of the cosine distance and the mean squared error between the pseudo-label and the input image is applied as a loss function to enhance the quality of the dehazed image. The most significant advantage of the SZDNet is that it does not require a large dataset for training before performing the dehazing task. Extensive testing shows promising performances of the proposed method in both qualitative and quantitative evaluations when compared with state-of-the-art methods.
Dynamic Graph Representation Learning with Neural Networks: A Survey

In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs allow to efficiently handle applications such as social network prediction, recommender systems, traffic forecasting or electroencephalography analysis, that can not be adressed using standard numeric representations. As a direct consequence of the emergence of dynamic graph representations, dynamic graph learning has emerged as a new machine learning problem, combining challenges from both sequential/temporal data processing and static graph learning. In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years. This paper aims at providing a review of problems and models related to dynamic graph learning. The various dynamic graph supervised learning settings are analysed and discussed. We identify the similarities and differences between existing models with respect to the way time information is modeled. Finally, general guidelines for a DGNN designer when faced with a dynamic graph learning problem are provided.
Independent Relationship Detection for Real-Time Scene Graph Generation

The current scene graph generation (SGG) task still follows the method of first detecting objects-pairs and then predicting relationships between objects-pairs. This paper introduces a parallel SGG thought that decouples relationship detection and object detection. In detail, we propose an independent visual relationship detection method, ‘Relationship You Only Look Once’ (RYOLO), which calculates relationships directly from the input image. For SGG, we present Similar Relationship Suppression and Object Matching Rules to match relationships and detected objects. In this way, the relationship detection and object detection can be calculated in parallel, and detected relationships can easily cooperate with detected objects to generate diversified scene graphs. Finally, our thought has verified the feasibility on the public Visual Genome dataset, and our method may be the first to attain real-time SGG.
Le molestie sessuali online tra adolescenti: che cosa sono e come si manifestano

Sexual harassment is a complex behaviour, characterized by several dimensions and definitions, as shown by the literature. In an online context, it is important to fully understand its nature, characteristics and consequences as well as how it may affect adolescents. This work aims to systematize the information available in the literature, by describing the main characteristics of the phenomenon, and trying to provide a definition. The research, which was conducted on four databases in November 2021, led to the identification of 20958 works. After removing the duplicates, and analyzing the abstracts and full-texts, a final number of 65 works were included, which were read in full-text and analysed. As for the studies collected, the type of relationship between the aggressor and the victim is described, alongside a description of the behaviors classified as online sexual harassment. Online sexual harassment can manifest itself in three main typologies (verbal, visual, cybersexual). Within these typologies, it is possible to identify three prevalent behaviours: unwanted solicitation, non-consensual sexting, and sharing of intimate and/or private content with third parties without the victim’s consent.
MLS-net: An efficient convolutional network for multi-scale ship detection in SAR images

In this paper, we consider the problem of ship detection in synthetic aperture radar (SAR) images. We proposed a new efficient convolutional neural network (CNN)-based detector with the multi-level supervision (MLS) strategy for multi-scale ship detection in SAR images, referred to as MLS-Net. In MLS-Net, an extremely lightweight backbone network is firstly designed using consecutive residual structures to capture SAR image features in the forward pass. Then, the feature fusion sub-network with three multi-scale feature aggregation modules is introduced to extract high-level semantic features. At last, the MLS-based detection heads are applied on feature maps with different scales to train our CNN in multi-level and then enhance features of multi-scale ship targets in SAR images. Experiments based on the open SAR ship dataset SSDD show that our proposed method achieves the smaller model size and similar detection performance compared with the state-of-the-art methods.
Pose estimation and occlusion augmentation based vision transformer for occluded person re-identification

Occluded person re-identification (ReID) is a challenging task as person images suffering from occlusion of various obstacles in real surveillance camera scene. Extracting partial feature from person image is crucial for occluded person ReID. In this paper, we propose the Pose Estimation and Occlusion Augmentation Based Vision Transformer (POVT) which leverage Pose Estimation Guided Vision Transformer (PEGVT) and an Occlusion Generation Module (OGM) to extract discriminative partial features. PEGVT divides the patch embeddings into different areas by pose estimation results and guide key point tokens to interact with corresponding patch embeddings to extract key point partial features. OGM can simultaneously generate realistic occlusion data which can improve the robustness of the ReID model, and occluded mask information which can supervise the finetune of pose estimation model to alleviate the performance degradation caused by domain gap. Experimental results over occluded re-identification datasets validate the effectiveness of the proposed POVT.
Utilizing digital technologies for waste management

This article is aimed at applying a new mechanism for improving an automated control system through methods for creating useful products from waste and developing innovative projects in a digital economy.
A Summary of the Development of Speech Recognition Technology

Speech recognition technology is an important technology aimed at realizing the free dialogue between artificial intelligence and human beings. Speech recognition technology still has very important research value in the 21st century. In this paper, the development history of speech recognition technology is described in chronological order, the classification of speech recognition technology is explained from the principle and application of speech recognition, and the principle and process of speech recognition technology are explained in detail, the relevant methods of speech recognition technology are introduced emphatically, and the experimental results of different methods of speech recognition technology are compared Finally, the application of speech recognition technology and the future development of speech recognition technology are illustrated. This paper mainly introduces the methods of speech recognition technology based on hidden Markov model, and also summarizes the methods of artificial neural network and artificial intelligence. It also points out the existing problems of speech recognition technology, and expresses relevant views on the development of speech recognition technology in the future.
A Novel Privacy-Preserving Framework Based on Blockchain Technology to Secure Industrial IoT Data

The Internet of Things (IoT) exhibits vast applications to the healthcare industry. However, there are various security vulnerabilities associated with such kinds of data. In order to avoid such kinds of security problems, the design of a distributed model to provide resistant to security breached and vulnerabilities. This provides a trust-less solution to the healthcare practitioner and their patients. One of the proposed solutions for adopting blockchain capabilities is in an Internet of Things (IoT) infrastructure. Blockchains provide tamper-proof and non-repudiations features to data. Moreover, the consensus mechanism of the blockchain protocol provides reliability and trust to the user to agree upon a certain transaction or updating, it’s also called proof of concept (PoC). In this research we have used hyper-ledger fabric as a tool for consortium blockchain design. We implemented the proposed model through chaincode, and we used cipher for threat and security check. The results are compared with the benchmark models, and it’s evaluated that the proposed model provides better privacy preservation.
Application of Data Mining Using the K-Means Clustering Method in Analysis of Consumer Shopping Patterns in Increasing Sales (Case Study: Abie JM Store, Jaya Mukti Morning Market, Dumai City

This study applied Data Mining method to cluster sales transactions at Abie JM Stores experienced a decline in sales.Therefore, a strategy was needed to increase sales again.One way that can be done to determine customer needs is to analyze sales transaction data.The sales transaction data can be further processed to obtain more helpful information to increase income, sales, and purchase turnover.Data mining by using k-means grouping or clustering.Data mining can be used to find.solutions in making sales decisions to increase revenue.Sales data storage stores a large number of sales transaction records, where each record provides products purchased by consumers in each sales transaction.From the calculation results, it can be concluded that the k-means clustering method can support the system well.Therefore we need a data processing process using a data mining technique.This study's data collection process uses the interview process and shopping transaction data collection
Emotional Development and Integral University Education

Currently university students live a series of problems, both external and internal to the educational institution that induce them to dropout school and/or poor academic performance and to highly vulnerable social development in the professional, work and even personal context. In the external context: violence, loss of values, injustices and even alterations in the order of the ecological system among others and in the context within the educational institution can be mentioned the low motivation of the student, the lack of knowledge of study techniques, the weakness of knowledge prior to the corresponding point of his school career, the low resilience to the changes presented in his personal context; factors (all of them) that keep him in a critical situation of stress. The purpose of this study was to analyse the need for an integral formation that can be implemented and endowed with socio-emotional tools to the student. Through the documentary analysis it is found that from the socioformation each one of the actors of the educational process must form a community in continuous learning that lives the elements of an emotional education that contributes to the social.
Experiencia del componente práctico del curso de proyecto de diseño I

The training of industrial designers based on the virtual and distance modality creates a synergy that allows us to chart a new course in education, this is part of the transformation and social mobility of millions of people, this modality is important within our educational process, managing to reach many places where training cannot be accessed and especially in industrial design, being able to bring design closer to many people and contexts is an important challenge that the program has outlined, for this strategies have been implemented which have allowed different students from the territory have access to training.&#x0D; &#x0D; This article exposes some of the results of the exercise of carrying out the development of the practical component of the Design Project I course in the virtual modality.
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network

The evolution of convolutional neural networks (CNNs) can be largely attributed to the design of its architecture, i.e., the network wiring pattern. Neural architecture search (NAS) advances this by automating the search for the optimal network architecture, but the resulting network instance may not generalize well in different tasks. To overcome this, exploring network design principles that are generalizable across tasks is a more practical solution. In this study, We explore a novel brain-inspired design principle based on the core-periphery property of the human brain network to guide the design of CNNs. Our work draws inspiration from recent studies suggesting that artificial and biological neural networks may have common principles in optimizing network architecture. We implement the core-periphery principle in the design of network wiring patterns and the sparsification of the convolution operation. The resulting core-periphery principle guided CNNs (CP-CNNs) are evaluated on three different datasets. The experiments demonstrate the effectiveness and superiority compared to CNNs and ViT-based methods. Overall, our work contributes to the growing field of brain-inspired AI by incorporating insights from the human brain into the design of neural networks.
Research on "Internet Standardized" Garbage Sorting Market Operation Method--Taking University Garbage Sorting as an Example

With the advent of the Internet era and the improvement of people's living standards, more and more garbage is generated per capita. In order to explore the market-oriented operation of garbage sorting in China, this paper takes universities as an example and creates the "Internet+standardized" university garbage sorting market-oriented operation alliance to test the effect of garbage recycling projects in universities. In the initial stage of the project, the unique conditions such as strong environmental awareness, huge amount of domestic waste, high recyclable rate and regular work and rest are the inherent advantages for promoting waste classification reform. In the implementation process, this paper will establish an integrated platform benefiting schools, students, merchants and recycling enterprises, form a market-based mechanism with a multi-body linkage for resource recovery and reuse, raise the environmental awareness of college students, and explore a suitable path for waste separation to be implemented in China.
Bone Tumor Detection and Classification Using Deep Learning

Abstract: Bone tumor is a rare type of tumor that refers to an abnormal growth of tissue inside and outside of the bone, with a high chance to spread to other parts of the body. A bone tumour is a mass growth of tumour cells that causes the disease and is difficult to diagnose. Children, teenagers, and young adults who have received a lot of radiation are the most affected. There are no known causes of bone cancer, unlike other cancers such those of the breast, lungs, prostate, stomach, and brain. Our project uses techniques like green plane extraction, arithmetic operations, linear contrast stretching, histogram equalization, and global thresholding to do segmentation, and GLCM is utilised for classification.
Optimized control for high-fidelity state transmission in open systems

Quantum state transfer (QST) through spin chains has been extensively investigated. Two schemes, the coupling set for perfect state transfer (PST) or adding a leakage elimination operator (LEO) Hamiltonian have been proposed to boost the transmission fidelity. However, these ideal schemes are only suitable for closed systems and will lose their effectiveness in open ones. In this work, we invoke a well explored optimization algorithm, Adam, to expand the applicable range of PST couplings and LEO to the open systems. Our results show that although the transmission fidelity decreases with increasing system-bath coupling strength, Markovianity and temperature for both ideal and optimized cases, the fidelities obtained by the optimized schemes always outweigh the ideal cases. The enhancement becomes more bigger for a stronger bath, indicating a stronger bath provides more space for the Adam to optimize. This method will be useful for the realization of high-fidelity information transfer in the presence of environment.
Design and Implementation of English Reading Online Examination System

This paper will propose a design architecture scheme of English reading online examination system, which mainly includes three modules: students, teachers and administrators, and can meet the related requirements of English reading online examination. In addition, the design will also analyze and explain the related contents of database design and system security design, aiming at putting forward a safe, efficient and practical design framework scheme for the current online English reading exam. Generally speaking, compared with the existing online English reading examination system, the online English reading examination system proposed in this paper introduces the activation code module, which makes the system bind with English reading textbooks. Teachers can also set the class examination time and unit arbitrarily through the system, enhance the fine-grained statistical effect of online English reading examination, improve teachers' work efficiency and reduce the examination cost.
Design and Analysis of Software and Hardware Cooperative Mode

In daily work, there is a large number of remote document signing requirements. At present, the mainstream method is to mail the signed documents or transmit pictures of the signed documents. Obviously, mailing some confidential documents is not conducive to confidentiality, and the picture transmission of the signed documents is easy to leak and cannot guarantee the signature of the person. Combined with the above scenarios, a secure remote document signing solution is urgently needed in the current market. Based on the design idea of hardware and software collaboration,we introduce the domestic Hummingbird E203 processor with RISC- V architecture as the core controller, use FPGA (field-programmable gate array) to realize the convolutional neural network handwritten character recognition hardware accelerator and digital watermark embedding algorithm, and carry out functional simulation and experimental data collection.