Reviewerly/exHarmony huggingface dataset:
Self-supervised denoising of Nyquist-sampled volumetric images via deep learning

PurposeDeep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits their utility. Here, we develop an algorithm (noise2Nyquist) that leverages the fact that Nyquist sampling provides guarantees about the maximum difference between adjacent slices in a volumetric image, which allows denoising to be performed without access to clean images. We aim to show that our method is more broadly applicable and more effective than other self-supervised denoising algorithms on real biomedical images, and provides comparable performance to algorithms that need clean images during training.ApproachWe first provide a theoretical analysis of noise2Nyquist and an upper bound for denoising error based on sampling rate. We go on to demonstrate its effectiveness in denoising in a simulated example as well as real fluorescence confocal microscopy, computed tomography, and optical coherence tomography images.ResultsWe find that our method has better denoising performance than existing self-supervised methods and is applicable to datasets where clean versions are not available. Our method resulted in peak signal to noise ratio (PSNR) within 1 dB and structural similarity (SSIM) index within 0.02 of supervised methods. On medical images, it outperforms existing self-supervised methods by an average of 3 dB in PSNR and 0.1 in SSIM.Conclusionnoise2Nyquist can be used to denoise any volumetric dataset sampled at at least the Nyquist rate making it useful for a wide variety of existing datasets.
A hybrid optimized multi-population flower pollination algorithm for web service composition problem

With the rapid development of Service-Oriented Computing (SOC), Web services have become the preferred technology for realizing Service-oriented computing problems and other related goals. How to find a cheap and high-quality Web service composition from a large number of Web services that provide the same function, that is, the research based on Quality of Service (QoS) is the most important problem in the Web service composition optimization model, which is also very important to improve the efficiency of the service composition. In this work, we use the intelligence optimization algorithms to search for the best combination of web services to achieve the functionality of the workflow’s tasks. And we propose a novel approach, called A Hybrid Optimized Multi-Population Flower Pollination Algorithm (AHOMFPA) to solve this problem. Empirical comparisons demonstrate AHOMFPA has advantages over other existing algorithms in efficiency and feasibility.
Remote sensing image segmentation network based on attention mechanism feature fusion

For remote sensing image segmentation projects, it is usually necessary to segment the boundary of ground objects finely, but the fitting of the boundary is a difficult problem in deep learning image segmentation. At the same time, for remote sensing images taken from high altitude, usually many large-scale targets in natural scenes will become small target objects in remote sensing images. To solve this problem, in order to improve the recognition effect of remote sensing image scene targets with huge scale differences, this paper combines UNet and FPN. This paper uses the framework of pytorch1.7.1 to establish the UNet + FPN model. Through the experimental results and visual analysis of building data sets and road data sets, the model in this paper has obvious advantages.
MANIFESTATIONS OF THE MAIN FUNCTIONS OF FINE ART IN CHILDREN'S ARTWORK

The article analyzes the main functions of art in relation to children's artistic creativity. Children's visual activity is not a conscious creative act, but a result of a naive worldview. Therefore, it is important to study the characteristics of perception from a psychological point of view in order to understand the mechanism of its influence on the emergence of a creative message in preschool children. The psychological basis of artistic creativity and its motivation is the aesthetic attitude to the world, which is a dominant personal characteristic of the individual and manifests itself in his or her work. The article touches upon the problems of visual perception associated with life experience and notes the spontaneity of children's creativity. Particular attention is paid to the functions of art, how they appear in children's art and what role they play in their formation and development. The article deals with cognitive educational, value-oriented, aesthetic, hedonistic functions in the light of children's artwork. The author comes to the conclusion that artistic inclinations exist in everyone, but most brightly and spontaneously they are manifested in children. The images created in the unconscious are an important part of understanding social processes in society. The process of cognition of reality plays an important role in the development of creativity. Children's art vividly displays the aesthetic sense, the sense of rhythm, colour and shape. Children are able to transfer their visual observations to the realm of visual arts.
Research on Key Technology of Safety Measure Control in Substation Large Overhaul Site

A key technical solution for safety control of large-scale maintenance site measures in open substations is proposed.Firstly, the semantic analysis technology framework of the safety measures scheme is constructed.Natural language processing technology is applied for recognition.A novel Multi-TFC network model is proposed, which can simultaneously complete three sub-tasks of work ticket classification, frame extraction and text detection, and realize automatic generation of substation work tickets to safety measures steps.At the same time, it adopts real-world modeling technology for overall 3D modeling of substations, integrates the deep learning model for the abovementioned work ticket to safety measures program generation, develops subsystems and display interfaces for work ticket and safety measures step generation of the safety measures control platform, receives intelligent analysis and calculation results of safety measures based on the transmission of the safety measures control platform, and establishes a location mapping database model of actual spatial coordinate positions and twin 3D scenes, and automatically generates 3D fences and security measures through The basic data automatically generates 3D fence and grounding line.Finally, it realizes the intelligent visualization presentation of the safety measures layout map.
A survey of image encryption algorithms for biomedical images

Biomedical imaging is a powerful tool for visualizing the body's internal organs and illnesses. Image protection is difficult in the field of biomedical imaging. Many studies have been conducted in the medical field to protect medical images. Encryption is the perfect solution for image privacy without data loss. Due to data size, redundancy, and performance limitations, traditional encryption methods cannot be directly applied to e-health medical data, especially when patient data is transferred through open channels. Thus, patients may experience a loss of confidentiality of the data content because images differ from text due to two different factors, such as loss of data and loss of privacy. Researchers have determined such protection risks and suggested several image encryption strategies for undesirable security difficulties. However, the study found that the currently offered approaches face application-specific security issues. Therefore, there is a growing demand to protect sensitive information in medical images. This paper presents a color and grayscale medical image encoding algorithm to preserve medical images. The compression and encoding performance of the proposed algorithm are analyzed and evaluated based on Mean Squared Error (MSE), Peak Signalto- Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), statistics, diversity, and entropy analysis. Matrix Laboratory (MATLAB) results show that the proposed algorithm provides high-quality reconstructed images with a good level of security during transmission.
Psychological Counseling System Based on Association Rules Mining Algorithm

With the rapid development of society, people's life and work pressure is increasing. How to relieve psychological pressure, express inner true emotions, and avoid the formation of mental illness is an urgent problem to be solved at present become the focus of society. The traditional way of psychological counseling usually adopts face-to-face communication. Some counselors do not want to share their thoughts with experts directly, which makes counseling impossible. When there are too many counselors, the counseling experts have no time to deal with them, resulting in low counseling efficiency. Therefore, the psychological counseling system designed in this paper can be consulted online, avoiding the embarrassment of communication, protecting the privacy of the counselors, and improving the efficiency of counseling services. The system combines the association rule algorithm, which can calculate the correlation degree of the information data of the consultants in the system, and generate consultation data reports for the consultants, so that experts can provide targeted suggestions for the consultants according to the report results.
Searchable Encryption Scheme Based on Multiple Access Control Authority Attributes

Searchable encryption technology searches data without disrupting the user’s encrypted ciphertext. Existing searchable encryption technology schemes has too little research on the control of access rights of users. In order to focus on protecting data security, the existing security schemes have to sacrifice data sharing, which loses most of the value of using cloud server (CS) for storage. This paper constructs an encryption scheme that can control access rights. It uses the key to quantifying the data to ensure the security of the data in the cloud. It uses public access control structure and the shared key encryption keyword to realize the query and verification of the data. Unauthorized users can do not get and decrypt the ciphertext data on the CS. Finally, the security proof shows that this scheme can ensure the security of user keywords and trapdoors.
A Mobile-Based Dynamic Approach to Comparative Study of Some Classification and Regression Techniques

With the ever-growing complex world of Machine Learning algorithms and data processing techniques, the entry requirements for beginners are steadily rising. So, to allow a much lower entry ceiling into the world of Machine Learning, an Android application is proposed in this study that informs the user about the performance of a few particular supervised algorithms for any given data set. This study informs on three main aspects, namely an Android application that can be developed on flutter as the front end, an API that handles the upload of Comma Separated Values (CSV) file, processing of data, and sending the results back to the front end, and the Machine Learning pipeline that automates the data preprocessing, model creation, and model evaluation for the classification task. The data preprocessing includes data preparation with SMOTE, missing values filling, Scaling and Transformation of data, Feature Engineering, and Feature Selection. To build models, libraries such as Scikit-Learn, XGBoost, and LightGBM are used. Finally, when the user uses the application, they only need to upload a CSV file with the label of the data set renamed to “Target.” As soon as the file is uploaded, the server starts the automation process. Once the results are formed, the view of the application changes to a new screen where the results are displayed as a table.
Data Fabric and Data Mesh in a Hybrid Cloud Landscape

In this chapter, we investigate additional facets of both concepts that arise specifically from hybrid cloud deployments. We briefly review the term hybrid cloud and how it relates to on-premises. Although cloud services are rated very highly in importance, they nevertheless create new challenges regarding access, integration, and consumption of data and AI assets across the organization. What does Data Fabric architecture and Data Mesh solution mean in the context of a hybrid cloud landscape, and what are the key challenges, for instance, related to addressing data and AI governance and trustworthy AI?
Ontological development of a competency based and learning objective oriented measurement tool

This study aimed to define the concepts and learning objectives related to the polygons sub-learning area of the 7 - grade mathematics course as ontology-based and to develop a measurement tool based on this ontology within the framework of competency ontology. In this study, we determined and included the learning objectives in the ontology alongside the concepts and prerequisites. The most crucial difference that distinguishes this study from others is that it is objectively oriented. Throughout the study, we followed ontology development processes that met the system's requirements based on the ontology development. In this context, we saw that the ontology developed answered the intended competency questions. We think this study will make very important contributions in terms of bringing a different perspective to measurement and assessment in mathematics education.
Context-Guided Multi-view Stereo with Depth Back-Projection

Depth map based Multi-view stereo (MVS) is a task that focuses on taking images from multiple views of one same scene as input, estimating depth in each view, and generating 3D reconstructions of objects in the scene. Though most matching based MVS methods take features of the input images into account, few of them make the best of the underlying global information in images. They may suffer from difficult image regions, such as object boundaries, low-texture areas, and reflective surfaces. Human beings perceive these cases with the help of global awareness, that is to say, the context of the objects we observe. Similarly, we propose Context-guided Multi-view Stereo (ContextMVS), a coarse-to-fine pyramidal MVS network, which explicitly utilizes the context guidance in asymmetrical features to integrate global information into the 3D cost volume for feature matching. Also, with a low computational overhead, we adopt a depth back-projection refined up-sampling module to improve the non-parametric depth up-sampling between pyramid levels. Experimental results indicate that our method outperforms classical learning-based methods by a large margin on public benchmarks, DTU and Tanks and Temples, demonstrating the effectiveness of our method.
Transfer learning based effective emotional face recognition using DCNN via cropping techniques

Facial Expression Recognition (FER) has grown in popularity as a result of the recent advancement and use of humancomputer interface technologies. Because the images can vary in brightness, backdrop, position, etc. it is challenging for current machine learning and deep learning models to identify facial expression. If the database is small, it doesn't operate well. Feature extraction is crucial for FER, and if the derived characteristics can be separated, even a straightforward approach can help tremendously. Deep learning techniques and automated feature extraction, allow some irrelevant features to conflict with important features. In this paper, we deal with limited data and simply extract useful features from images. To make data more numerous and allow for the extraction of just important facial features, we suggest innovative face cropping, rotation, and simplification procedures and advocate using the Transfer Learning technique to construct DCNN for building a very accurate FER system. By replacing the dense top layer(s) with FER, a pretrained DCNN model is adopted, and the model is then modified with facial expression data. The training of the dense layer(s) is followed by adjusting each of the pre-trained DCNN blocks in turn. This new pipeline technique has gradually increased the accuracy of FER to a higher degree. On the CK+ and JAFFE datasets, experiments were run to assess the suggested methodology. For 7-class studies on the CK+ and JAFFE databases, high average accuracy in recognition of 99.49% and 98.58% were acquired.
Scrutinize and Discover of Image of Freshwater Taken by Faraway Realizing Using FFNN and ConvNet Mechanisms

Water is the most momentous for all types of species, this need is notably more predominant for anthropoids, and this is since blood in the anthropoid body requires about 90% of water. The quantity of fresh water is on globe remnants constant, but the inhabitants are just too sweeping, this is why there is a more scarcity of freshwater and it is extensively spoken among the people. It is therefore salient to unambiguously gauge the amount of freshwater is on the globe. To estimate it, first take the image utilizing remote sensing and then to discover the information about the water in the image, some mechanism utilizing in image processing is utilized. First, it segregates the attribute highlights of the image to precisely assess the information in the image about water, and then it is trouble-free to discover the objects or scrutinize the objects or formation it meaningful in the image through the image section with those attributes. Therefore, two arrangements are utilized in this paper to recognize these operations more precisely. This paper utilizes the Feed Forward Neural Network (FFNN) system for segregating attributes and then the CNN mechanism for segmentation.
Eco-Toxicological Risk Assessment of Heavy Metals and Polycyclic Aromatic Hydrocarbons in Sediments of Tigris River, Iraq

The Tigris River is an important source for domestic, agricultural, industrial, fisheries, and ‎even recreational activities. Therefore, environmental assessment of river sediments is vital, as sediments can be considered ‎reservoirs of pollutants that are re-released into the water during floods or human activities. ‎This research aimed to assess Tigris river sediments‎ ‎ from Al ‎Qayyarah to Al Tarmiyah.‎ Nine samples of sediment were collected for analysing seven heavy metals and a metalloid (i.e. Cu, Cd, Cr, Pb, Zn, Ni, and As) and sixteen PAHs compounds. The concentrations of heavy metals Cd, Pb, As, Cr, and Ni in some locations have exceeded ‎their background concentrations in the earth's crust. For assessing the contamination of sediments by metals, three indices, viz, SPI, TRI, and MERMQ were used. According to the results of the three indices, Al ‎Qayyarah‎ and Baiji sites were more contaminated than other sites. This may be attributed to anthropogenic activities represented by the petroleum industry which enriches sediments with metals. Also, three indices for evaluating the environmental risk of PAHs, i.e., MERMQ, ‎SQGQ‎, and RQ, were used. Based on Mean ERM Quotient values, the toxicity probability of PAHs in sediments ranged from 49% at Al ‎Qayyarah and Baiji to 21% and 9% at other sites. The SQGS index values showed that Al ‎Qayyarah‎ and Baiji sites‎ were highly affected by PAHs contaminants.‎ ‎According to the Risk Quotient (RQ) values of the sum PAHs, the environmental risk of river sediments ranged from low to moderate.
Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade

Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning.

A quick forecast of the region’s structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model’s outcomes show that elastic net regression is the most effective predictive model for predicting building damage.
Attacks on Social Media Networks and Prevention Measures

Social Media, usually referred to as a social space, provides the best platform for people from various physical places to communicate, share images, interests and ideas or meet new friends. And due to this, the number of users is also increasing day by day. Social Media Networks confront a concerning rate of rise in cyber threats due to their enormous user base and quick rise in popularity. The producers of Social Media Networks intend to protect their users, but hackers and invaders are able to get beyond the security mechanisms by employing a variety of ways to exploit users’ privacy, identity, and confidentiality. It’s possible that the majority of users of social networking sites are ignorant of the presence of these serious hazards. The primary security concerns relating to online social networks are highlighted in this paper including the existing techniques for preventing those cyber attacks.