Web Client Research Articles

Three-Dimensional Web-Based Client Presentation of Integrated BIM and GIS for Smart Cities

Smart cities use technological solutions to reduce the drawbacks of urban living. The importance of BIM and GIS integration has increased with the popularity of smart city and 3D city concepts in recent years. In addition to 3D city models, Building Information Modeling (BIM) is an essential element of smart cities. The 3D city model web client in this study displays three-dimensional (3D) city models created using photogrammetric techniques, BIM, and campus infrastructure projects. The comparison and integration of the aforementioned systems were evaluated. A web-based 3D client framework and implementation for combined BIM and 3D city models are the goals of the submitted work. The Web is a very challenging platform for 3D data presentation. The Cesium engine based on HTML5 and WebGL is an open-source creation and the virtualcityMAP application using the Cesium infrastructure was used in this study.

Investigation of Microsoft Teams and Skype as Distance Education Tools ın Terms of Digital Forensics

necessity has disrupted traditional norms and has affected all aspects of life. Undoubtedly, the education sector is one of the most significant areas affected by technology. As the population rate increases, the importance and necessity of education also increase. In response to this need for education, new educational models emerge based on the contemporary conditions. One of the critical factors that lead to the emergence of these new models is the COVID-19 outbreak that affected the entire world in March 2020. As a result of this epidemic, countries have adopted various methods in the field of education and training. Some countries suspended their educational activities, while others have continued the process through distance education. Distance education is a teaching method that relies heavily on the active use of information and information tools. The extensive use of distance education tools can attract the attention of malicious individuals who may seek to access these services directly or indirectly. This study aims to present the data and information obtained from analyzing the traffic information stored by Microsoft Teams and Skype or incoming and outgoing traffic information over the network in an isolated environment. The study was conducted on computers with the Windows 10 operating system and web client applications installed. One of the primary goals of this study is to assist researchers who will study distance education tools in the future.

Receive wireless sensor data through IoT gateway using web client based on border gateway protocol

One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.

Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs

The field of interior home design has witnessed a growing utilization of machine learning. However, the subjective nature of aesthetics poses a significant challenge due to its variability among individuals and cultures. This paper proposes an applied machine learning method to enhance manufactured custom doors in a proper and aesthetic home design environment. Since there are millions of possible custom door models based on door types, wood species, dyeing, paint, and glass types, it is impossible to foresee a home design model fitting every custom door. To generate the classification data, a home design expert has to label thousands of door/home design combinations with the different colors and shades utilized in home designs. These data train a random forest classifier in a supervised learning context. The classifier predicts a home design according to a particular custom door. This method is applied in the following context: A web page displays a choice of doors to a customer. The customer selects the desired door properties, which are sent to a server that returns an aesthetic home design model for this door. This door configuration generates a series of images through the Unity 3D engine module, which are returned to the web client. The customer finally visualizes their door in an aesthetic home design context. The results show the random forest classifier’s good performance, with an accuracy level of 86.8%, in predicting suitable home design, marking the way for future developments requiring subjective evaluations. The results are also explained using a feature importance graphic, a decision tree, a confusion matrix, and text.

Digitally improving UK police surveillance and incidence response using real-time crowd reporting app: Digipolice

Overtime, evolving technologies has made ways of monitoring and controlling crime and action in the United Kingdom (UK) become more challenging which needs optimization with the help of technologies. Also with the rate of crime across cities in the UK like mass shootings, murder and gang gun battles, the UK Police system within localities are overwhelmed or may not efficiently cope with crime alerts and incidences coming in at increasing frequencies. However looking in the area of real time crowd reporting, there is need to leverage technology to enhance public safety, improve incident response, and foster community engagement. This necessitated the aim of this study to develop a digital policing surveillance app for crowd reporting in the UK. By utilizing digital policing surveillance apps, the aim is to improve crowd management capabilities by enabling real-time reporting of incidents and observations from both the public and law enforcement personnel. This was positioned to facilitate better situational awareness, efficient resource allocation, and effective response strategies. To achieve the objectives, Python Programming Language 3.8 were deployed together with a Django Framework and a Microsoft Visual Studio Build Tools. In the DP system, the Model-View-Controller (MVC) pattern, which divides an application into three interconnected components—the model, the view, and the controller—was used for the architectural design of the system. For the database, Django's ORM makes it simple to support many databases, and in this project straightforward SQLite3 database were utilised which is ideal for small-footprint applications. Using WebSockets to establish real-time, bidirectional communication between web clients and servers, the user interface and intelligence board for the system were developed. A clean designed frontend design system with Bootstrap version 5—an open-source, reusable design framework created by Twitter—was selected for its easy user interface. The system was put through 50 concurrent load tests with actual users swarming the app since the DP app was subjected to hundreds of people complaining in real time. 2. The host computer had a Core i5 vPro CPU running at 4GHz and 4GB of RAM. The result shows that 50 users successfully generated an average of 29.7 requests per second. The system scaled up nicely to 50 users and reached its highest response time of 2600 milliseconds (2.6 seconds) in total. A dramatic rise in activity and a longer response time occurred after there were more than 50 users. The system could not operate at its peak efficiency with 100 users because response times were inconsistent and exceeded 7000 milliseconds (7 seconds) for each request. The effectiveness of the online deployment was evaluated with respectable results, and the system requirements also underwent successful testing. The appropriate investigation of ethical issues was done to meet requirements. DigiPolice has the potential to develop into a cutting-edge programme for real-time crowd surveillance and incident reporting. UK Police can employ it to as advancement to its “My Police” with as fast-track real-time and collaborative crowd reporting enhancements.

Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

Abstract This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily.

Blockchain-Based System and Methods for Sensitive Data Transactions

A blockchain-based processing framework for sensitive data is proposed. The smart network comprises a blockchain module, an electronic contract (E-contract) layer node, and a Software-as-a-Service (SaaS) layer module. The underlying blockchain module provides technical support, such as virtual machines, consensus algorithms, transaction verification mechanisms, and accounting mechanisms. The E-contract layer module provides a distributed application service and uses the blockchain technology (BT) to support it. In addition, it runs the code of the sensitive data transaction system and the code created by the algorithm for encoding generation at the E-contract layer. The SaaS layer module offers a cloud-platform service that allows each party to easily engage in business collaboration via web portals or clients. The proposed smart system is used by each party get involved in the production of sensitive data. The final sensitive data are produced by the final data generator, and other modules involved in the process of data production are unaware of the final data. This approach prevents the leakage of sensitive data into the circulation.

Impact of E-Commerce in Bhutan: Issues & Challenges

Electronic commerce is more than simply one more approach to support or to develop running businesses. E-commerce has brought revolutionary changes across the globe. It is a complex invention that is instantly changing the conventional method of running a business. E-commerce is paving the way for huge business improvement in Bhutan. Expanding web clients have further added to its improvement. E-commerce has made the online travel industry grow through numerous methods and included another business boulevard through the online retail industry in Bhutan. The current study has been attempted to portray the present scenario and facilitators of E-Commerce in Bhutan, dissect the present patterns of E-Commerce and look at the obstructions of E-Commerce in Bhutan.

Quality of Chronic Obstructive Pulmonary Disease Information on the Chinese Internet: Website Evaluation Study.

The development of internet technology has greatly increased the ability of patients with chronic obstructive pulmonary disease (COPD) to obtain health information, giving patients more initiative in the patient-physician decision-making process. However, concerns about the quality of website health information will affect the enthusiasm of patients' website search behavior. Therefore, it is necessary to evaluate the current situation of Chinese internet information on COPD. This study aims to evaluate the quality of COPD treatment information on the Chinese internet. Using the standard disease name "" ("chronic obstructive pulmonary disease" in Chinese) and the commonly used public search terms "" ("COPD") and "" ("emphysema") combined with the keyword "" ("treatment"), we searched the PC client web page of Baidu, Sogou, and 360 search engines and screened the first 50 links of the website from July to August 2021. The language was restricted to Chinese for all the websites. The DISCERN tool was used to evaluate the websites. A total of 96 websites were included and analyzed. The mean overall DISCERN score for all websites was 30.4 (SD 10.3; range 17.3-58.7; low quality), no website reached the maximum DISCERN score of 75, and the mean score for each item was 2.0 (SD 0.7; range 1.2-3.9). There were significant differences in mean DISCERN scores between terms, with "chronic obstructive pulmonary disease" having the highest mean score. The quality of COPD information on the Chinese internet is poor, which is mainly reflected in the low reliability and relevance of COPD treatment information, which can easily lead consumers to make inappropriate treatment choices. The term "chronic obstructive pulmonary disease" has the highest DISCERN score among commonly used disease search terms. It is recommended that consumers use standard disease names when searching for website information, as the information obtained is relatively reliable.

ENABLING INTEROPERABILITY OF URBAN BUILDING ENERGY DATA BASED ON OGC API STANDARDS AND CITYGML 3D CITY MODELS

Abstract. This paper presents an investigation into the interoperability of 3D building energy data management, delivery, processing, and visualization via web clients using Open Geospatial Consortium – Application Programming Interface (OGC API) standard-based data models and web interfaces. Specifically, the OGC API – 3D GeoVolumes enable access to 3D city model geometries and semantics on the web, the OGC API – Features support the 2D version of the same geospatial data, the OGC API – Processes are used for CityGML analytics and building energy computation with the SimStadt urban simulation software and the OGC SensorThings API is utilized to manage related spatiotemporal or time-series datasets. The efficacy of this approach has been demonstrated in the OGC Testbed 18 Innovation Program, which highlighted the capacity of OGC API web services to synchronize building energy data and computation results between client and server for the case study of Helsinki, Finland, and Montreal, Canada. The advantages of using OGC API services for 3D building energy data interoperability are discussed, and it is suggested that the use of OGC API be promoted to the general public as well as extended to other domains and on a larger scale in future research.

Development of client applications on Blockchain

Objective. The article discusses issues related to the process of developing client Web applications for IT projects, which are based on the principles of decentralized network technology - blockchain. The authors explore practical ways to implement the user authorization process and its interaction with the blockchain network through a client application.Method. The MetaMask browser extension was chosen as a research tool, which allows you to develop, test and run your own software modules.Result. In the course of the study, the analysis of MetaMask tools was carried out, the processes associated with the start of work were considered both from the point of view of the developer and from the point of view of the wallet user. Understanding the organization of these processes will allow users to form the necessary knowledge in the development of client applications built on blockchain.Conclusion. The program code templates developed by the authors can be used as standard ones in the development of client applications in the decentralized Ethereum network. The advantage of the proposed solutions lies in the use of simple software structures that allow the formation of basic principles.

Advanced Examination of User Behavior Recognition via Log Dataset Analysis of Web Applications Using Data Mining Techniques

As web systems based on containerization increasingly attract research interest, the need for effective analytical methods has heightened, with an emphasis on efficiency and cost reduction. Web client simulation tools have been utilized to further this aim. While applying machine learning (ML) methods for anomaly detection in requests is prevalent, predicting patterns in web datasets is still a complex task. Prior approaches incorporating elements such as URLs, content from web pages, and auxiliary features have not provided any satisfying results. Moreover, such methods have not significantly improved the understanding of client behavior and the variety of request types. To overcome these shortcomings, this study introduces an incremental approach to request categorization. This research involves an in-depth examination of various established classification techniques, assessing their performance on a selected dataset to determine the most effective model for classification tasks. The utilized dataset comprises 8 million distinct records, each defined by performance metrics. Upon conducting meticulous training and testing of multiple algorithms from the CART family, Extreme Gradient Boosting was deemed to be the best-performing model for classification tasks. This model outperforms prediction accuracy, even for unrecognized requests, reaching a remarkable accuracy of 97% across diverse datasets. These results underline the exceptional performance of Extreme Gradient Boosting against other ML techniques, providing substantial insights for efficient request categorization in web-based systems.

An Online AI-Powered Interactive Histological Image Annotation Platform for Analyzing Intestinal Regenerating Crypts in Post-Irradiated Mice

The goal of this project is to build an online AI-powered interactive annotation platform to accurately and efficiently annotate intestinal regenerating crypts in histological images of mice after abdominal irradiation. The proposed platform is developed by the seamless integration of a front-end web client and a back-end server. Such client/server design allows the users to access the platform without software installation on local computers. Our front-end client is developed with SvelteJS + WebGL technology stack, allowing access from any common web browsers and enabling user interaction, such as image importing/visualization, interactive crypt annotating, and annotation saving/deleting. The back-end server is responsible for executing the tasks requested from the web client, for instance, image pre-processing, AI-based crypts automatic identification, and database management. The image preprocessing is designed to extract a single cross section image using morphological operations because multiple hematoxylin and eosin (H&E) stained jejunum cross sections from post-irradiated mice are scanned within one slide. The auto-crypt identification is powered by a trained and validated AI engine U-Net, classifying image grid tiles into two groups with and without regenerating crypts. The database is implemented with the self-contained SQLite to support recording and indexing the annotated grid tiles with regenerating crypts. The workflow for crypt analysis on this interactive platform has 5 steps: 1) manually import a whole H&E slide image; 2) auto-preprocess the slide by extracting single cross-section images; 3) auto-identify regenerating crypts with an AI engine; 4) interactively annotate (add, delete, modify) auto-identified crypt markers; 5) save and/or output the annotation to the database or the local drive. The performance of the developed interactive crypt analysis platform was evaluated in aspects of accuracy and efficiency. The AI-powered crypt auto-identification accuracy was assessed by computing the mean absolute error (MAE) on crypt number per cross section between manual and auto annotation using a testing dataset containing 80 cross sections. It achieved an MAE of 3.5±4.8 crypts per cross section, and 81.25% of the cross sections have no more than 5 crypts difference. The efficiency was assessed under two conditions with the server on the cloud and a local computer. It took about 2-3 minutes to finish the entire workflow on the cloud, while 1-2 minutes on the local by saving ∼1 minute on image uploading. The developed web client/server platform enables online automatic identification and interactive annotation of mice crypts in minutes. It is a convenient tool that allows accurate and efficient crypt analysis and can be extended for other histologic image analyses.

A Study on Maize Leaf Pest and Disease Detection Model Based on Attention and Multi-Scale Features

The detection and accurate positioning of agricultural pests and diseases can significantly improve the effectiveness of disease and pest control and reduce the cost of prevention and control, which has become an urgent need for crop production. Aiming at the low precision of maize leaf pest and disease detection, a new model of maize leaf pest and disease detection using attention mechanism and multi-scale features was proposed. Our model combines a convolutional block attention module (CBAM) with the ResNet50 backbone network to suppress complex background interference and enhance feature expression in specific regions of the maize leaf images. We also design a multi-scale feature fusion module that aggregates local and global information at different scales, improving the detection performance for objects of varying sizes. This module reduces the number of parameters and enhances efficiency by using a lightweight module and replacing the deconvolutional layer. Experimental results on a natural environment dataset demonstrate that our proposed model achieves an average detection accuracy of 85.13%, which is 9.59% higher than the original CenterNet model. The model has 24.296 M parameters and a detection speed of 23.69 f/s. Compared with other popular models such as SSD-VGG, YOLOv5, Faster-RCNN, and Efficientdet-D0, our proposed model demonstrates superior performance in the fast and accurate detection of maize leaf pests and diseases. This model has practical applications in the identification and treatment of maize pests and diseases in the field, and it can provide technical support for precision pesticide application. The trained model can be deployed to a web client for user convenience.

MapSafe: A complete tool for achieving geospatial data sovereignty

AbstractSensitive geographic data are invaluable assets for the people to whom they belong and their disclosure should be decided by the sovereign data owner (SDO). Due to several high‐profile data breaches and business models that commercialize user data, the need for new approaches to geoprivacy and data sovereignty has grown. We propose MapSafe, a client web application that first obfuscates datasets using donut masking or hexagonal binning, separately, and thereafter implements a multi‐level encryption scheme that permits SDOs to share the final encrypted volume containing the geospatial information when they choose and at a level of detail which they are comfortable. The authenticity verification of the volume is facilitated by storing the hash value corresponding to the encrypted volume immutably on the Blockchain as a public record. Our approach places geoprivacy under data guardians'’ control, and its integration capabilities promote its adoption in existing and future geospatial web systems.

Management of Colombian hotel companies' websites: an empirical approach

The article examines hotel companies' Website management in Cúcuta, Colombia. The methodology is quantitative with descriptive design. A survey of sixty-two items is applied to the managers of five hotels, to know their opinion on aspects such as: Website, Prices, Reservation, Products, Navigability, Customer Relationship Management Variables, Privacy and Security. The results indicate that hotels manage their websites well. However, weaknesses are evident in the visualization of room services, information on city attractions, interaction via chat and access for the visually impaired. We conclude with the need to constantly review the changing trends in web pages and clients' demands, in order to offer information that provides optimum visibility on the national and international market.

Fake News Law Enforcement Efforts During the Campaign Period

One of the despicable acts that can be done online is spreading fake news (fraud). In the midst of a Scam campaign, the point is to make open conclusions, lead assumptions, form assertions, and have fun testing the insights and rigor of web and social media clients. Cases of fraud in the middle of the campaign period often occur and spread so quickly because of the variables that are easily accepted by society. In order for data dissemination to be effective by word of mouth spreading through online media very quickly, this research was conducted in the city of Yogyakarta

Автоматизація в системі електронних закупівель з модулем аукціону

The article focuses on design and development around automation processes in complex e-procurement system with auction module. The complex system was built based on microservices architecture using latest server-side and client-side technologies. Data synchronization and integration amongst system’s various modules was implemented in real time via Web Socket protocol and database triggers. Web client notification methods involved SignalR technology. Data was stored in a distributed database and cached to dramatically improve query execution time. Authentication and authorization for users, modules, and subsystems was designed according to OAuth 2.0 standard with a proprietary implementation of the Proof Key Code Exchange algorithm and Backend for Frontend approach. The web client was built as a single page application using a strongly typed managed language to simplify development and debugging process. The client application was optimized with the help of lazy-loading algorithms for key modules, among them supplier, buyer, and common module. The system was composed into a Docker container to be ready to publish on cloud services or physical servers and support any operating system. A set of unit, integration, and system tests was created for each subsystem and module. Monitoring of the complex includes a technology stack to log, store, and visualize request statuses, latency, uptime, and overall health of the system. Existing automation flows for build and deployment were improved to accommodate the needs of the complex e procurement system. The system was designed and built for key stakeholders such as private enterprises acting as buyers and people or companies acting as suppliers.

Harbinger: A Toolkit for Vulnerability Testing of IoT Devices and Web Clients

Abstract: The need for efficient tools to identify and mitigate vulnerabilities has never been more critical due to the complexity and proliferation of online applications and embedded systems. We introduce a Python toolkit in this study that combines a variety of security testing and analysis methodologies, allowing users to locate and fix web application vulnerabilities and examine firmware files. Our toolkit offers aggregated reports summarizing the outcomes of firmware analysis and web application vulnerability testing, in addition to being adaptable and user-friendly so customers may change and add features to meet their unique needs. We provide the findings from testing and analysis carried out on a variety of online apps and firmware files, as well as the design and implementation of our tools. The integration of additional security testing tools, compatibility for other file formats, and the introduction of machine learning-based analysis are just a few of the future directions we could take our toolset. This Python toolkit makes a significant addition to the realm of cybersecurity by offering customers a strong and adaptable solution for web application vulnerability testing and firmware analysis.

Design of Fruit-Carrying Monitoring System for Monorail Transporter in Mountain Orchard

The real-time monitoring and detection of the fruit carrying for monorail transporter in the mountain orchard are significant for the transporter scheduling and safety. In this paper, we present a fruit carrying monitoring system, including the pan-tilt camera platform, AI edge computing platform, improved detection algorithm and the web client. The system used a pan-tilt camera to capture images of the truck body of the monorail transporter, realizing monitoring of fruit carrying. Besides, we present an improved fruit carrying detection algorithm based on YOLOv5s, taking the “basket”, “orange” and “fullbasket” as the object. We introduced the improved attention mechanism E-CBAM (Efficient-Convolutional Block Attention Module) based on CBAM, into the C3 module in the neck network of YOLOv5s. Focal loss was introduced to improve the classification and confidence loss to improve detection accuracy; to deploy the model on the embedded platform better, we compressed the model through the EagleEye pruning algorithm to reduce the parameters and improve the detection speed. The experiment was performed on the custom fruit-carrying datasets, the mAP was 91.5%, which was 9.6%, 9.9% and 12.0% higher than that of Faster-RCNN, RetinaNet-Res50 and YOLOv3-tiny, respectively, and detection speed at Jetson Nano was 72[Formula: see text]ms/img. The monitoring system and detection algorithm proposed in the paper can provide technical support for the safe transportation of monorail transporter and scheduling transportation equipment more efficiently.