Reviewerly/exHarmony huggingface dataset:
&lt;i&gt;PHP 8 release&lt;/i&gt; to upgrade library websites

The author explores the possibility to update PHP language up to PHP 8 in the library websites. Such upgrade is determined by the need for recommended safety level for web-resources. As exemplified by St. Petersburg library websites, the role of PHP language is demonstrated: 70% library websites are based on this technology. The content management systems (CMS) and PHP language versions are identified. For the three most popular CMS (CMS WordPress, CMS Joomla!, without CMS), technological recommendations are given on how to transfer to the new PHP versions (as for early 2021). About 28% websites do not use CMS. Their compatibility with PHP new version has to be provided individually. Approx. 28% library websites use CMS Joomla!, and 19% – CMS WordPress. Today, these management systems cannot support PHP 8 in full: WordPress is beta-compatible, while with Joomla! the work has been just in progress. The most recent information for IT specialists interested in library website design is given; releases to follow PHP 8 after November 2020, are described.
From distinguishability to informativity

We present a study of the distinctiveness of random and non-random texts based on text characteristics of quantitative linguistics. We additionally experiment with text features that evaluate contiguity associations among sentences by means of BERT (Bidirectional Encoder Representations from Transformers). To this end, we experiment with generative models for random texts as currently discussed in the context of neural networks. The chapter contributes to the clarification of deficits of existing random text models and of the informativeness of quantitative text features.
Efficient Look Based Pin Validation Using Gaze Based Pin Entry

The major goal of this work is to propose a Real-time application for Password Authentication using Eye Tracking, with a Gaze-Based pin entering employing an intelligent camera for eye tracking and detection to identify the password pin. PIN’s, or Personal Identification Numbers, are primarily used for end-user security and verification. When a security pin is physically entered for password authentication, this raises the risk of PIN information being stolen via shoulder surfing and thermal tracking. Another highly secure method of authentication is a Gaze-Based PIN entering that does not require physical touch. This one-of-a-kind method of identification is based on a channelized disrupted image frame that tracks the eye centre on a regular basis.
Controlled generation of self-sustained oscillations in complex artificial neural networks

Spatially distinct, self-sustained oscillations in artificial neural networks are fundamental to information encoding, storage, and processing in these systems. Here, we develop a method to induce a large variety of self-sustained oscillatory patterns in artificial neural networks and a controlling strategy to switch between different patterns. The basic principle is that, given a complex network, one can find a set of nodes-the minimum feedback vertex set (mFVS), whose removal or inhibition will result in a tree-like network without any loop structure. Reintroducing a few or even a single mFVS node into the tree-like artificial neural network can recover one or a few of the loops and lead to self-sustained oscillation patterns based on these loops. Reactivating various mFVS nodes or their combinations can then generate a large number of distinct neuronal firing patterns with a broad distribution of the oscillation period. When the system is near a critical state, chaos can arise, providing a natural platform for pattern switching with remarkable flexibility. With mFVS guided control, complex networks of artificial neurons can thus be exploited as potential prototypes for local, analog type of processing paradigms.
Virtual Control Panel API: An Artificial Intelligence Driven Directive to Allow Programmers and Users to Create Customizable, Modular, and Virtual Control Panels and Systems to Control IoT Devices via Augmented Reality

An incremental tide in IoT devices prompts a centralized control system where physical controls such as buttons and knobs can become virtual and centralized via augmented reality: especially headset AR. I conduct a literature mapping to identify gaps and create a front-end based virtual control panel API tool to allow programmers and end-users to rapidly create headset augmented reality-based virtual control systems for physical devices. I probe on how an artificial intelligence back-end aids in such a system in terms of panel creation and automation as well as panel usage and control scenarios. I delve into use cases of the virtual control panel API including mass usage and specialized scenarios. Furthermore, I specify the guidelines for a requirements elicitation study for such a system along with potential points of derivation. Next, I probe into implications and limitations of such a system in terms of artificial intelligence, usage, and precision. My work has implications for programmers and users of a virtual control system backed by artificial intelligence for the purpose of controlling physical IoT devices in mass usage as well as in specialized scenarios.
Investigations on Numerical Techniques for Detecting Variations in Acoustic Emissions

The objective of this paper is to present a hybrid methodology of analysing acoustic signals arising in industrial processes through comparisons of known numerical techniques such as clustering. Apart from data acquisition and pre-processing, the other essential component of using acoustics is to design an analysis methodology, that can culminate in practical applications. This paper applies Gaussian Mixture Models and Self-Organising Maps to cluster pre-processed AE hits obtained from acoustic sensors in the form of tensile, shear and mixed modes of compression on a material. For an in-depth analysis, custom features such as high peak regions, low peak regions, strongly hit and weakly hit signals are introduced to compare with the clusters formed. The results show that for small AE signals that are obtained or extracted after events detection, a time-domain based clustering can be applied and used for isolating similarities and distinctions among the signals belonging to the same group.
Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion

Deep fusion networks have received considerable attention lately due to the growing adoption of IoT devices, smartphones, and wearables that incorporate multiple sensing modalities, and their promising applications from human activity recognition to smart home automation. Despite recent advances in this area, there are several practical requirements that are often overlooked. Specifically, fusion networks must maintain their performance during momentary and long-term changes in the environment, be robust to sensor data quality issues, and have a reasonable size so that they can be deployed on resource-constrained devices. My PhD research aims to address these challenges by building robust multimodal fusion networks that rapidly generalize to new environments and have a smaller number of trainable weights, hence lower memory and carbon footprints.
FABEMD based Innovative Watermarking Method

Telemedicine is overcoming obsession and is now a thriving research area. Commuting therapeutic images between distant locations is a common occurrence in telemedicine, as therapeutic images are often used by remote specialists to make inferences about diagnosis. The majority of therapeutic photographs are sent through the internet. When commuting over the internet, therapeutic photos can be attacked by various types of noise. The use of noise-affected therapeutic images may result in a misdiagnosis. As a result, the remote authority must vouch for the validity of the significant component (ROI) in the therapeutic picture and retrieve the ROI if it has been harmed by noise. This paper introduces a ground breaking watermarking method that uses Fast and Adaptive Bi-dimensional Empirical Mode Decomposition (FABEMD) to recover the ROI in a therapeutic picture when it is attacked by noise. Experiments with this novel technique have shown that the ROI in therapeutic images can be returned to its original state.
Development of a System for Detection, Diagnosis, and Treatment for Eye Strabismus Disease

Strabismus is one of the most common eye disorders caused by a problem with the eye muscles. It is difficult to determine the degree of Strabismus in the two eyes. Reduced vision and a failure of the eyes to function together correctly, especially for 3D vision, are symptoms of this eye illness. This project aims to develop a system able to help the ophthalmologists to detect, diagnose and treat eye Strabismus and eliminate the use of glasses and some laser sessions and medicaments, which very expensive and causes pain to the patient. In the first step, signals acquired from the eyes via electrodes, were fixed on the patient's face and processed to extract specific information, whereas the gravity center (GC) of the eye movements represents the most important feature in this step. Then we use the calculated Gravity center to determine the level of vision deviation that is directly related to a problem in the eye muscles. At the next step, the ophthalmologists treat the patient using the same device through several physical exercises like games performed by the patient on the device. The obtained results demonstrate the ability of our system to classify between the normal and abnormal patient, to determine the level of abnormality or the deviation percentage, to determine the level of eye vibration and to treat many that eye disease.
Multimedia music teaching system based on collaborative recommendation algorithm

With the popularization of quality-oriented education and comprehensive education in schools, China has put more energy into art education. The traditional teaching mode exists in a single way, and the content lacks change, which leads to poor information transmission in a short time. In this paper, a multimedia music teaching system based on Web Service is proposed based on collaborative recommendation algorithm. The system is based on B/S three-tier architecture and developed by JSP, Visual Studio 2008 and SQL server. The streaming media format and network transmission protocol of the multimedia music teaching system are analyzed and the multimedia music teaching system is designed. The main function modules of the multimedia music teaching system are realized, and some modules of the system are tested. The system running results show that the system runs well and meets the original design goals.
Reinforcement Learning for Robust Optimization: An Application in Kidney Exchange Programs

Kidney Exchange Programs allow an incompatible patient-donor pair, whose donor cannot provide a kidney to the respective patient, to have a transplant exchange with another pair in a similar situation. The associated combinatorial problem of finding such exchanges can be represented by a graph: nodes represent incompatible pairs and arcs represent compatibility between donor in one pair and patient in the other. This problem has some uncertainty, which in the literature has been commonly addressed in the following ways: expected utility, fall-back mechanisms and robust optimization. We propose an alternative interactive tool to support decision makers (DMs) on choosing a solution, taking into account that some pairs may become unavailable. For a given solution the predicted performance is evaluated under multiple scenarios generated by Monte Carlo Tree Search (MCTS). The root node of the tree corresponds to no failures. From there, a tree of failure possibilities is generated, each of them corresponding to a different scenario. A solution is determined for every particular scenario. At the end, each solution is evaluated under each scenario. Scenarios are grouped based on the cardinality of the set of failing vertices, and average results for each cardinality are considered. Finally, Pareto dominated solutions are filtered out and the non-dominated average solutions are displayed and compared with the worst case scenario. The tool visually drives DMs in the process of choosing the best solution for their particular preferences.
Infographics and visual aspects in the training processes of PetrSU students

Introduction. The amount of information offered for study in modern educational institutions is rapidly increasing. Overcoming the inconsistency of the obligation to master the increasing flow of educational information and achieving the necessary "level" of training in fulfilling the traditional requirement of pedagogy – withstanding an acceptable amount of visibility leads to the mass introduction of multimedia tools. The folding so-called clip thinking shades the problems of filling thesaurus learning information virtually uncontrollable content and quality. Materials and Methods . Theoretical and empirical methods of research were used in the course of the work: analysis, synthesis, generalization, comparison, comparison, scientific theorization. Results. Clip-thinking interferes with a clear understanding of the context, and therefore the clip leaves no trace in semantically related phenomena. The trend of radical change in the roles of teachers and students reveals the main reasons and conditions for the transition to visualized presentation of educational information. In addition to the apparent negative consequences of the current pedagogical situation, some advantages of the protective reactions developed by modern learners to the most powerful stream of educational information are revealed. Studies have confirmed the risks of over-visibility in the learning process. Objective differences in the purpose and effectiveness of the types of visibility considered are highlighted. The main points of the formation of visual images are reflected, the obligatory maintenance of the conditions of problems in the educational process is emphasized. Discussion and Conclusions. The studies that have been carried out present an argument for the recommendations for the proposed frame-graphic approach in the organization of the educational process. The orientation of the educational process for each allocated group is justified, which requires a rather different nature of the script. One solution is to use educational information visualization tools to ensure the sustainability of the creative thinking vector.
Optimizing Recurrent Neural Network Using Neuromodulation

Over the past years, there has been a rapid pace in the field of technology. It is fostering itself at an ever-rising rate and machine learning is a part of this entire revolution. Technology is moving closer and closer as machines are taught how to learn and grow on their own. With seamless evolution in engineering advancements, machines are more enduring gradually, manageable, and consistently exhibiting better results. Although these techniques are achieving new highs, all of them still lack the continuity of past activities and carry them forward with new novel activities, similar to how the human - brain performs. To unleash the limitations of current neural networks and design a better approach like the brain a neuromodulation technique is applied to differentiable plasticity. In this method, we will embrace meta-learning through memorization and reconstruction of the novel images. Consequently, we are comparing the task with other previously existing neural techniques to evaluate the performance. Rooting out the limitations of gradient explosion, vanishing gradients, and time the complexities of training LSTM models. We conclude that neuromodulation on differentiable plasticity is a compelling framework for complex neural networks.
TORA: Text Summarization Using Optical Character Recognition and Attention Neural Networks

Text Summarization is the process of creating a short and coherent version of a longer document that holds the same meaning as that of the original data. This article illustrates the technique to read the text in a printed document (such as newspaper, brochure, web document, etc.) and generate a summary of text. The method proposed is named Text Summarization using Optical Character Recognition and Attention Neural Networks (TORA). TORA can perform extractive summarization of a news article with the aid of Recurrent Neural Networks, Bidirectional Long Short-Term Memory, and Bahadanu Attention Network. The experimental results of the proposed method are promising. The experimental results have shown 80% accuracy in producing the summary from the large text document.
High-Payload Data Hiding Scheme Based on Ordered Shifting of AC Coefficients for JPEG Images

Information security is a crucial issue in the modern data communication environment. Hiding secret data in multimedia is one of the emerging techniques in this research area. By leveraging the popularity of JPEG image format, we propose a data hiding scheme for JPEG compressed images. Several researches have been proposed to hide secret message in the AC coefficients of JPEG codes. However, they restricted the target of embedding to several predefined values of coefficients in order to preserve the file size or improve visual quality of the stego-image. In this paper, we propose a new data hiding scheme based on an ordered shifting of the AC coefficients. With no restriction imposed on the target value, a large amount of data can be embedded without compromising the imperceptibility and increasing the file size significantly. The proposed scheme is compared with the related works in terms of the hiding capacity, the visual quality, and the increase of file size. Good experimental results are obtained.
Estimation of Human Posture Using Convolutional Neural Network Using Web Architecture

Human posture estimate is a technique for determining the position of the body from a single, often monocular image. There are varieties of CNN architectures are available for human pose estimation. This paper presents a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks. Essentially, it is indeed a set of parameters that can be related to define the pose of the individual. A proper relation between two components is known as a pair (or a limb). Nevertheless, there are many considerations such as layer thickness and their width, number of feature maps, kernel size, batch size, etc. In this article, the performance assessment of CNN is performed by constructing a basic architecture with image classification. We tested the output of our network architecture on the most popular image repository called CNN used for the identification and classification mission. The research findings reveal that the suggested network delivers the highest classification accuracy as compared to current techniques. Finally identified results describes the better understand of CNN models for a variety of image classification missions. Furthermore, the convolutional neural network (CNN) is the category of deep neural networks which has now been commonly used in visual recognition.
File Operations Comparative Analysis of various Guest Virtual Machine’s on Xen Hypervisor

Virtual machine (VM) is an isolated domain that gives off an impression of being an entire PC yet in reality just approaches a bit of the PC assets. Execution of the virtual machine running on a similar PC framework equipment relies upon the exhibition of the host working framework. In this work execution of various visitor VM's on a similar host working framework has been analyzed. Here in this detailed experimentation or comparative analysis, Ubuntu, a Linux based operating system has been utilized for host working framework and for guest working frameworks Ubuntu v14.04, CentOS v6.3 operating systems has been utilized. Execution estimation of virtual operating system has been completed in the equivalent controlled scenarios for both (Ubuntu and Centos) guest operating systems utilizing benchmark applications. In this paper Input Output file operations have been performed on various virtual machine using benchmarking tool (Sysbench) to analyze the performance of various guest (Visitor) VM and compare them.
GDT-Net: A Model Based on Deep Learning for Water Surface Garbage Detection

Traditional water surface garbage detection algorithms have the shortcomings of low detection accuracy, slow speed, and susceptibility to interference. Therefore, this paper proposes a new single-stage object detection model called GDT-Net, which is divided into three parts: feature extraction network, feature fusion network, and detection head. In the feature extraction network part, a new cross-stage multi-scale partial feature extraction module (CSMP) is designed by combining CSPNet and Res2Net. It enhances the learning ability of CNN without increasing the number of parameters. The feature fusion network uses PANet and adds a newly designed multi-scale atrous convolutional enhancement module (MACM) and an improved attention model (IAM) to PANet to focus and enhance the valuable features while enhancing the semantic information of shallow features. The experimental results show that the Precision and Recall of GDT-Net are 89.3% and 92%, respectively, and the mAP reaches 91.1%, and the FPS is 38, which is better than the mainstream object detection networks such as Faster RCNN and YOLOv3 in terms of detection accuracy and speed.
Intelligent Acquisition Method of UAV Maneuver Knowledge Based on the Leapfrog Algorithm

Knowledge acquisition is the premise of autonomous decision-making and control of UAV. It is difficult to acquire explicit knowledge and tacit knowledge at the same time by current methods. Therefore, this paper uses leapfrog algorithm to study the intelligent acquisition method of UAV maneuver knowledge. Firstly, the paper introduces the typical leapfrog algorithm, determines the characteristic attributes of flight data, designs the frog locations updating policy and fitness assessment approach, so that the number of attributes contained after solving the problem is not too simple or redundant, and carries out the simulation experiment of a typical aircraft maneuver case rule knowledge extraction and bucket action rule knowledge extraction. The simulation results demonstrate that proposed intelligent algorithm can be effectively applied to the acquisition of UAV maneuvering knowledge.
The Cloud Computing Adoption Factors in Various Organizations: A Systematic Literature Review

Cloud computing offers so many advantages to the organizations such as availability, and cheaper cost. Many organizations are adopting cloud computing. However, the decision to adopt cloud computing is affected by several factors. Therefore, it is important to investigate the most common factors that affect the decision of organizations to adopt cloud computing. This systematic literature review helps to identify the main factors that affect the decision on adopting cloud computing by organizations. From our search in different databases such as ACM Digital Library, IEEE, SpringerLink, Scopus, etc., we select around 150 articles from the field of cloud computing and around 30 articles were qualified to be reviewed to help achieve the goal of this paper. For the purpose of this systematic literature review article, the criteria were set to select only the studies which followed the Technology Organization Environment (TOE) framework and were published from year 2014 to 2020. This review paper has identified the main factors and concerns that influence the decision to adopt cloud computing. Considering the importance of cloud computing, we encourage that future research to be on the awareness of organizations about cloud computing.