Reviewerly/exHarmony huggingface dataset:
Optimization of the PHP Interactive Interface of a College Online Educational Platform in a Quantum Heterogeneous Cloud Environment

Aiming at the Grover quantum search simulation algorithm, this paper proposes the structure of the unitary operator in the Grover algorithm, and proposes a compression method of the Oracle operator and the phase-shift unitary operator. Through theoretical analysis and experimental verification, it is proved that the compression ratio of this method is 1:8, and the online platform of colleges and universities is designed and implemented using Apache +PHP +Postg re sql. It is divided into four parts: the construction of the system application platform, the design of the system structure, the realization of the system function and the application effect. An unsupervised clustering method based on quantum theory is proposed, which is similar to the principle of quantum and potential energy change. potential energy function to determine cluster centers.
Advances in operation and finance in supply chains

Operations and finance are two sides of the same coin. In supply chains, operations management aims to match the supply with demand of material flows, whereas corporate finance seeks to match the supply with demand of monetary flows. With increasing availability of huge amounts of data, operations and finance interfaces in a cycle of material, financial, and information flows. For example, Fintech is re-shaping supply chains, as it has fueled innovative financial products and services that rely on extensive operational data. It has given rise to new business models aimed at directly improving operational performance by alleviating financial frictions, and it has disrupted traditional processes in the delivery of financial services, leading to a wide range of new opera-tional challenges and opportunities. In this special issue, 14 papers were subsequently extended, double-blind reviewed and revised according to the regular IJPE process. A brief summary of the papers is presented below, assigning the papers to categories that reflect their main characteristics. We hope that this gives a good impression of recent research streams in operation and finance in supply chains, and can motivate future studies. We express our sincere gratitude to Prof. T. C. E. Cheng and Prof. Stefan Minner for their strong support of this special issue.
Prefix Method of Parallel Pattern Search and Homogeneous Computing Matrix

This article focused on the development of matrix methods of pattern search with hardware implementation based on associative memory. The objective is to reduce the search time by forming a set of prefixes and their parallel processing with a set of associative computational matrices. The method uses dynamic reconfiguration of text character relationships so that the text as a char is alternately represented by matrix-string data structures. The method is based on parallel comparisons of a pattern and specially prepared prefixes with a matrix representation of the test. The comparison matrices obtained in the search steps allow us to calculate the amount of text shift by more than 1 character, which leads to a reduction in the search time. As a result, the original pattern is represented by a triangular matrix consisting of prefixes shifted to the right. The extreme prefix has a length of one character. The text for this method has a two-dimensional representation with the number of columns being equal to the length of a pattern. The representation of a pattern as a set of prefixes makes it possible to reduce the total number of steps by analyzing a variety of options for the initial search positions. The constructed computing matrix is based on combining the results of a parallel search and highlighting the priority position of a partial occurrence, excluding unproductive (blank) steps.
An Improvement to Test Case Prioritization Techniques Using Machine Learning

Regression Testing is an important process every time new changes are integrated in the software product. Managing test cases becomes important in this scenario, where testers need to retest or select test cases based on the changes incorporated. Machine learning can reduce the effort of testers by providing a simplified approach in test case management. Prioritization of test cases can be taken as an initial step of handling bulk test cases and arranging them on the basis of certain priority. Several methods were proposed in the past that handle test cases in case of regression testing. This paper proposes a novel methodology that uses unsupervised machine learning algorithm to prioritize the test cases. Methodology contributes toward risk-based approach whose objective is to prioritize the test cases by identifying the most risky areas first. It categorizes the test cases in clusters representing different priority level. Our proposed approach is based on four essential factors namely the Change-Module mapping, Degree of Coupling between effected modules, Test Coverage Reports and Test Execution Results. Research objective relies on exploring how dynamic change information when combined with white box testing parameters and unsupervised machine learning can be beneficial in categorizing test cases. Paper also presents analysis of the existing literature related to test case prioritization, identified research gaps and expected contribution toward the literature.
Dynamic Self-Supervised Teacher-Student Network Learning

Lifelong learning (LLL) represents the ability of an artificial intelligence system to learn successively a sequence of different databases. In this paper we introduce the Dynamic Self-Supervised Teacher-Student Network (D-TS), representing a more general LLL framework, where the Teacher is implemented as a dynamically expanding mixture model which automatically increases its capacity to deal with a growing number of tasks. We propose the Knowledge Discrepancy Score (KDS) criterion for measuring the relevance of the incoming information characterizing a new task when compared to the existing knowledge accumulated by the Teacher module from its previous training. The KDS ensures a light Teacher architecture while also enabling to reuse the learned knowledge whenever appropriate, accelerating the learning of given tasks. The Student module is implemented as a lightweight probabilistic generative model. We introduce a novel self-supervised learning for the Student that allows to capture cross-domain latent representations from the entire knowledge accumulated by the Teacher as well as from novel data. We perform several experiments which show that D-TS can achieve the state of the art results in LLL while requiring fewer parameters than other methods.
Big Multimodal Data Analysis: Applications

Big DataVol. 10, No. 6 Guest EditorialBig Multimodal Data Analysis: ApplicationsGwanggil Jeon, Valerio Bellandi, Abdellah Chehri, and Ernesto DamianiGwanggil Jeon*Address correspondence to: Gwanggil Jeon, Department of Embedded Systems Engineering, Incheon National University, Academiro 119, Incheon 22012, Korea, E-mail Address: gjeon@inu.ac.krhttps://orcid.org/0000-0002-0651-4278Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea.Search for more papers by this author, Valerio Bellandihttps://orcid.org/0000-0003-4473-6258Dipartimento di Informatica, Università degli Studi di Milano Statale, Milano, Italy.Search for more papers by this author, Abdellah ChehriDepartment of Mathematics and Computer Science, Royal Military College of Canada, Kingston, Ontario, Canada.Search for more papers by this author, and Ernesto DamianiDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE.Search for more papers by this authorPublished Online:7 Dec 2022https://doi.org/10.1089/big.2022.0217AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Big Multimodal Data Analysis: Applications." Big Data, 10(6), pp. 479–480FiguresReferencesRelatedDetails Volume 10Issue 6Dec 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Gwanggil Jeon, Valerio Bellandi, Abdellah Chehri, and Ernesto Damiani.Big Multimodal Data Analysis: Applications.Big Data.Dec 2022.479-480.http://doi.org/10.1089/big.2022.0217Published in Volume: 10 Issue 6: December 7, 2022Online Ahead of Print:November 11, 2022PDF download
Display of interactive 3D models in augmented reality on mobile devices

This paper study is related to two research areas, namely 3D computer graphics and augmented reality with a combination of their display on mobile devices. It presents the creation of three different interactive 3D models based on a realistically drawn image of domestic animals and can be displayed on mobile devices using augmented reality. The textured animals' models are displayed in the application Augmented animals (slo. Obogatene živali) with a simple user interface. The usability of the application is demonstrated by the detection of the image target, i.e., a printed interactive card, which proves the interaction between the mobile device and the augmented paper. When the mobile device camera recognizes the target, it displays the selected animal on the screen. The result is the enhancement of the real environment with animated 3D characters. By displaying a 3D character on the screen and interacting with the user interface, the presentation of each animal in three different animated movements is enabled. The first empirical part of this work was done with the help of the Blender program, in which we created all three animal 3D characters. First, we had to model all the animals from the initial templates into a recognizable 3D mesh, which we then mapped the textures on. This was followed by the construction of a system of bones and animation controls, based on which we could create the animal animations. After this step, we transferred the project to the Unity program. Then it followed the construction of an application that allows the representation of characters in augmented reality. The results of the entire work are appropriately made animal characters in the form of animated 3D models that can be displayed in augmented reality mode on mobile devices using interactive cards. The selected testing parameters showed that there are certain differences in rendering between the two tested mobile devices depending on the selected subdivision level of the 3D character. However, for recognition based on lighting conditions, distance and slope between the image target and the mobile device, the best user experience is obtained when the image target is captured from a distance of 15-20 cm and from a bird's eye view under good lighting conditions.
Modern Development and Challenges in Virtual Reality

Virtual reality (VR) is one of the technologies with the highest expectations for future growth. By creating realistic images and objects, a VR environment gives the user the impression that they are completely engrossed in their surroundings. VR applications that go beyond leisure, tourism, and marketing are now in high demand and thus the technology must be user-friendly and economical. The major technology firms are already striving to create headsets that do not require cables and that allow for high-definition viewing. Artificial intelligence is being used to control VR headsets that have far more powerful CPUs. The new standard will also offer some intriguing capabilities, like the ability to connect huge user communities and additional gadgets. Customers will be able to get photos in real-time in corporate settings, almost as if they were seeing them with their own eyes. This book presents a comprehensive overview of VR applications in medicine, electric vehicles, aviation, architecture, and more.
Spring Native

Recently, serverless computing has gained much traction. Serverless computing is a cloud computing model where the cloud provider provisions the required resources for running an application on demand. With this model, you only provide resources for the application when it’s actually in use. In traditional cloud computing models, when you allocate resources to a machine on any cloud provider, you must pay for the machine, even though the application is sitting idle. Serverless computing solves this issue because the resources are allocated only when the application is in use. No resources are allocated to an application if it's not in use.
Application of Mathematical Models in Forecasting the Usage of Water, Electricity and Gas in Hospital Logistics

To explore the prediction effect of mathematical models on water, electricity and gas usage in hospital logistics operations, in order to obtain the best prediction model and improve the level of hospital logistics refinement management. Monthly data of water, electricity and gas usage of hospital logistics from January 2016 to December 2020 were selected for the training and testing of the model, a nonlinear self-organizing regression neural network model and an exponential smoothing correlation model were constructed, and the prediction effect was evaluated and analyzed by evaluation indexes. The goodness-of-fit of the NARNN model reached 0.87047 on the training data set of water usage, 0.91166 on the validation set, and 0.94479 on the test set, with an overall model goodness-of-fit value of 0.88075, and an overall goodness-of-fit value of 0.95808, close to 1, in the prediction of gas usage, and the seasonal multiplication model in the water and electricity The RMSEs of the seasonal multiplication model in both water and electricity usage forecasting reached the lowest. All the mathematical models used in this paper can effectively predict the water, electricity and gas usage data. By evaluating the index results, a comprehensive comparison shows that the nonlinear self-organizing neural network model has the best prediction effect and can provide prediction data support for information decision making and energy saving and emission reduction deployment in the hospital logistics department.
Research of Face Image Preprocessing and Classification Based on Torch Module

Accelerating the preprocessing of large batches of face images before training is one of the bottlenecks that limit its real-time application effect. This paper proposes a method for processing face images: by sharpening the image, processing its gray-scale and small moment waves, it processes background of face images, making them more identifiable. Then using torch technology, it can reduce face images in large batches and improve the efficiency of processing tens of thousands of images for deep learning-based classification processing. After the two steps, it processes and accelerates classification of face images for different applications, such as office private documents and police station private documents. Comparing with the traditional method, the speed is increased by 1/3 and the processing and classification accuracy is over 93%.
Virtual Triplets: Human-Agent Shared Control of Virtual Avatars

We propose Virtual Triplets, a collaborative Virtual Reality system capable of human-human and human-agent interaction where users can choose to switch their control between two or more virtual avatars, while the virtual agent would take over the control of the free, unpossessed avatars. We developed a use-case scenario where a single human instructor concurrently supervises two students in a virtual classroom setting. Each student is assigned a personal instructor avatar where the instructor could switch the possession between these two avatars. When the human instructor attends to one of the students, the virtual agent controls the unpossessed avatar to assume the supervision role for another student. Virtual Triplets supports recording and playback of the user’s actions. For example, the human instructor’s demonstration shown to one student could be recorded and played back on a different avatar for another student. Moreover, the human instructor can choose to possess a top-down view instead of an individual avatar to have an overview of all the workspaces and give commands to the virtual instructor’s avatar. Our goal is to enable parallelism in a collaborative environment to improve the user experience when multitasking is required for repetitive activities.
Investigating the Effect of Superpower Priming and Representation on Virtual Reality Interaction

We investigate superpowers as a way to both present and visually represent interaction techniques in VR. A mixed-design study (n=20) compares variants of the well-known Go-Go interaction technique in a non-game selection task. The primary factors are the effect of using superhero-themed priming (including a brief backstory intervention and a modified avatar appearance), and modifying the visual representation of the interaction technique to be reminiscent of superhero powers.
Development of a Tourist Information Service Through Geolocation

A fairly common problem for tourists visiting a city is finding monuments of interest that are close to where they are. Normally, they resort to the use of guides or maps, with the limitations that this entails. This article describes an application that aims to facilitate a tourist’s visit to a city using mobile geolocation. The implemented system consists of a web application from which a database is maintained with all the monuments of a city and the associated information, and an Android app. The latter is used by tourists to retrieve monuments and places of interest from the database that are close to them using the latitude and longitude obtained by mobile geolocation. In this way, the tourist has a list of all the nearby monuments, and for each monument, he has useful information and resources such as audios, documents or images.
Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm

AbstractThe amount of data generated, collected, and processed through computer networks has increased exponentially in recent years. Network attacks have also become an inherent concern in complex networks as a result of this increase of data. The practise of assessing trust using attributes that influence trust is known as trust evaluation. It is confronted with a number of serious challenges, including a shortage of critical assessment data, a requirement for big data processing, a call for a simple trust relationship expression, and the expectation of automation. Machine learning (ML) has been applied to trust evaluation in order to overcome these issues and intelligently and automatically evaluate trust. This research propose novel technique in data fusion model with security and data optimization technique in edge computing. Here the proposed data fusion is carried out using secure sequential discriminant auto encoder in which the improvement of data accuracy, as well as for the maximizing of Edge-cloud based sensor networks lifespan. The fusion of edge cloud data has been carried out using discriminant auto encoder which is integrated with distributed edge cloud users, where the security of the network has been enhanced using secure sequential fuzzy based trust model. The data optimization has been established using Genetic swarm lightweight optimization algorithm. The experimental analysis has been carried out based on data fusion as well as network security model. The parametric analysis is carried out in terms of network security analysis, throughput, Coverage fraction, Delay time, Energy consumption, Storage efficiency.KeywordsData fusionSecurityData optimizationEdge computingTrust evaluation
Congregation of News in a Digitized World Using News Angle

Technology is rapidly growing and fast and instant network connections have changed life. The use of mobile, tablets, and laptops are increasing. In this fast and information-oriented world, we need to stay updated with every incident and news. The NewsAngle app is an android mobile application where users have access to the latest news from the Admin app. The application has focused on the existing system and tried to solve problems such as the improvised notification system, news with tags, and live news channel option using youtube APIs. A payment gateway is integrated into the system which will help to alter the users into two separate categories like paid and free users. Android studio used in applications supports programming languages like Java, Kotlin, and XML (Extensible Markup Language). The drag-and-drop feature for the user interface and the java code auto-completion feature for the backend have made android studio easy to use. The main focus of the application is to connect news articles from all around the world and deliver to them to users as fast as possible. It provides automation, immediacy, adaption, and personalization. The Application provides information about the stock market, mutual funds, and cryptocurrency. The user can see share market values and live television channels which are displayed in the application.
Dyslexic Reading Assistance with Language Processing Algorithms

The Dyslexic reading challenge is not completely resolved till date with advance learning algorithms. The need for breaking complex words and aiding the children to remember with hints is the need of the hour. The development of reading assistant with breakdown of complex words, using hints to remember backword and forward iterations, creative use of word cloud and using deep learning techniques to effectively tokenize and assist the struggling readers.
Frontiers of AI beyond 2030: Novel Perspectives

The motive of the study of Frontiers of Artificial Intelligence (AI) is to get a brief idea about the aspects of the theoretical approach in the field of AI after a decade. Artificial Intelligence can be defined as the field of computer science that automates machines to behave and act like humans using a set of roots and algorithms. The topics of interest involved are learning techniques such as Reverse Learning, Unlearning, Adaptive Machine Learning, and Deep Personalization with AI. In the scientific field, AI has become a new trend and adaptable tool. As a consequence of the tremendous expansion of AI in several aspects of modern existence, new approaches and learning methods are to be introduced and to be updated frequently. A few of the difficulties include data quality and availability, validations, and errors across various use cases. Architecture for interchange is required so that AI can spread dynamically into every potion of advanced life and learn from one another. The focus of the work has been on enhancing capabilities with AI in a variety of areas.