User Registration Research Articles

Blockchain-Assisted Access Control with Shuffled Shepherd Fire Hawk Optimisation-Key Generation for Privacy Protection in Cloud

Cloud is a model of computing providing on-demand availability of computer system resources, mainly data storage and computing power, without the user’s active management. This provides sharing and reduces user computing and storage costs. With the development of cloud scale and intensification, cloud security is becoming a vital issue in the cloud computing field. Here, a newly optimised hybridisation algorithm, namely, Shuffled Shepherd Fire Hawk Optimization (SSFHO) algorithm is proposed for key generation to initiate privacy protection in the cloud. The different entities involved in this approach are Data Owner (DO), Data User (DU), blockchain, and Cloud Service Provider (CSP). Here, the access control process includes seven categories, such as initialisation, registration, generation of key, data access control, authorisation, authorisation revocation and data protection. Also, registration is done in three phases, like registration of cloud, registration of user and resource publishing. Moreover, SSFHO is the integration among Shuffled Shepherd Optimization Algorithm (SSOA) and Fire Hawk Optimization (FHO) algorithm. The performance of the model is done with various performance metrics like, authorisation time, privacy rate, and memory. These revealed high values of privacy rate of 0.93, low values of authorisation time and memory of 0.559 and 0.843 GHz.

KCL TEST: an open-source inspired asymptomatic SARS-CoV-2 surveillance programme in an academic institution.

Rapid and accessible testing was paramount in the management of the COVID-19 pandemic. Our university established KCL TEST: a SARS-CoV-2 asymptomatic testing programme that enabled sensitive and accessible PCR testing of SARS-CoV-2 RNA in saliva. Here, we describe our learnings and provide our blueprint for launching diagnostic laboratories, particularly in low-resource settings. Between December 2020 and July 2022, we performed 158277 PCRs for our staff, students, and their household contacts, free of charge. Our average turnaround time was 16 h and 37 min from user registration to result delivery. KCL TEST combined open-source automation and in-house non-commercial reagents, which allows for rapid implementation and repurposing. Importantly, our data parallel those of the UK Office for National Statistics, though we detected a lower positive rate and virtually no delta wave. Our observations strongly support regular asymptomatic community testing as an important measure for decreasing outbreaks and providing safe working spaces. Universities can therefore provide agile, resilient, and accurate testing that reflects the infection rate and trend of the general population. Our findings call for the early integration of academic institutions in pandemic preparedness, with capabilities to rapidly deploy highly skilled staff, as well as develop, test, and accommodate efficient low-cost pipelines.

Blockchain-based access control architecture for multi-domain environments

Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.

Multi-sensor data fusion framework for energy optimization in smart homes

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.

Blockchain‐Based Piecewise Regressive Kupyna Cryptography for Secure Cloud Services

Cloud computing (CC) is a network‐based concept where users access data at a specific time and place. The CC comprises servers, storage, databases, networking, software, analytics, and intelligence. Cloud security is the cybersecurity authority dedicated to securing cloud computing systems. It includes keeping data private and safe across online‐based infrastructure, applications, and platforms. Securing these systems involves the efforts of cloud providers and the clients that use them, whether an individual, small‐to‐medium business, or enterprise uses. Security is essential for protecting data and cloud resources from malicious activity. A cloud service provider is utilized to provide secure data storage services. Data integrity is a critical issue in cloud computing. However, using data storage services securely and ensuring data integrity in these cloud servers remain an issue for cloud users. We introduce a unique piecewise regressive Kupyna cryptographic hash blockchain (PRKCHB) technique to secure cloud services with higher data integrity to solve these issues. The proposed PRKCHB method involves user registration, cryptographic hash blockchain, and regression analysis. Initially, the registration process for each cloud user is performed. After registering user particulars, Davies–Meyer Kupyna’s cryptographic hash blockchain generates the hash value of data in each block. When a user requests data from the server, a piecewise regression function is used to validate their identity. Furthermore, the Gaussian kernel function recognizes authorized or unauthorized users for secure cloud information transmission. The regression function results in original data by enhanced integrity in the cloud. An analysis of the proposed PRKCHB technique evaluates different existing methods implemented in Python. The results contain different metrics: data confidentiality rate, data integrity rate, authentication time, storage overhead, and execution time. Compared to conventional techniques, findings corroborate the assertion that the proposed PRKCHB technique improves data confidentiality and integrity by up to 9% and 9% while lowering storage overhead, authentication time, and execution time by 10%, 12%, and 12%.

Biometric lock with facial recognition implemented with deep learning techniques

The increased criminal activity associated with unauthorized entry into facilities has become a global concern. Traditional mechanical locks suffer from drawbacks such as key loss, theft, duplication risks, and time-consuming operation. Therefore, biometrics has been explored as a key to accessing a restricted area. However, some challenges still need to be solved in developing such systems, including user registration, response speed, maintainability, and the ability to distinguish between real and fake individuals. This paper proposes and develops a biometric lock system (BLS) whose opening is performed by recognizing a person?s face. It solves the challenges of re-training, antispoofing, real-time response, and works inside an embedding system. The proposed BLS overcomes these challenges using a pre-trained network called FaceNet for feature extraction and coding into 128-dimensional vectors.We use the characteristic vector provided by FaceNet and a cosine distance to recognize the persons. It also incorporates ResNet18 + remote photoplethysmography (rPPG) to avoid spoofing. The architecture was implemented in a BLS, demonstrating an impressive false acceptance rate of 0% under varying lighting conditions, with an average response time of 1.68 seconds from facial detection to door opening. The BLS has easy maintainable devices, providing enhanced security by accurately identifying individuals and preventing unauthorized access. The system can distinguish between real and fake people without using specialized hardware. Making it a versatile solution suitable for homes, offices, and commercial spaces. The results underscore the potential efficacy of our proposed BLS in mitigating security challenges related to unwarranted access to restricted facilities.

Online Public Opinion Analysis Model Based on Long Short-term Memory Network and Expectation Maximization

Abstract With the rapid dissemination of online public opinion, its emotions are easily transmitted to the general public. It possesses a certain level of social mobilization capacity and can impact the stability of society. To characterize the sentiment trend of social network information and determine its influence, we have proposed a method based on long short-term memory (LSTM) networks and expectation maximization(EM). This model employs a long short-term memory network for data training, obtaining the number of positive public opinions through word-to-word matching. Based on the expectation-maximization method and Jensen’s inequality, the lower bound of the maximum likelihood function is iteratively computed, ultimately achieving convergence of this likelihood function. This convergence value is then used for sentiment analysis. Our study utilizes 10,000 valid pieces of data from the Sina microblog as experimental data. By comparing our model with the K-MEANS model and the EM model, the results indicate significant improvements in the accuracy and convergence of our model. Our research discovers that the influence of public opinion increases as the compensation value for adoption rises, and the probability of public opinion generation gradually increases with the length of user registration years, eventually slowing down.

The Contingent Effects of IS Certifications on the Trustworthiness of Websites

Information systems (IS) research has largely treated IS certifications (i.e., graphical cues that prove the endorsement of independent third parties) as universally effective at improving website visitors’ perceptions of trustworthiness. However, inconclusive findings on the effectiveness of IS certifications on websites have emerged, critically challenging their usefulness. We seek to reconcile these inconclusive findings by drawing on swift trust theory and the notion of humans as cognitive misers. Specifically, we investigate whether the effects of IS certifications are contingent on visitors’ expectations and the website’s baseline trustworthiness (i.e., the original website before adding and visitors’ processing of IS certifications). Through a multistudy investigation combining an online (N = 191) and a follow-up field experiment with up to €4 million in sales volume (N = 306), we reveal the contingent effects of IS certifications on the trustworthiness of websites: Below (but not above) a certain level of a website’s baseline trustworthiness (i.e., the trust tipping point), IS certifications significantly increase trustworthiness. We also show that IS certifications do not increase the likelihood of user registrations (i.e., trust-related behavior) when a website’s baseline trustworthiness surpasses this trustworthiness threshold. Overall, we provide an important new perspective that explains and resolves previous inconsistent findings on the (in)effectiveness of IS certifications for trustworthiness and subsequent trust-related behaviors. We equip practitioners with valuable and actionable guidance on the usefulness of IS certifications to strengthen their digital businesses.

Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems.

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user's data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user's age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest.

CareerConnector

Abstract: Career Connector is an innovative online platform designed to facilitate efficient job searching and recruitment processes, connecting job seekers with potential employers. Powered by a robust technology stack, with .Net serving as the backend language, CareerConnector offers a seamless user experience for both job seekers and employers. Key functionalities encompass user registration and profile creation, advanced job search and filtering, streamlined job posting and application processes, user notifications, and an array of administrative tools for platform management. Additionally, the system incorporates advanced features such as resume upload and parsing, applicant tracking, machine learning-driven job recommendations, and user feedback and review systems. CareerConnector places paramount importance on data privacy and security, adhering to pertinent data protection laws while implementing stringent encryption measures. Scalability and performance optimization are integral to ensuring a smooth and responsive user experience, even as the platform expands. By embracing. Net as the backend language, CareerConnector benefits from its reputation for reliability, scalability, and a wealth of community support. .Net , including React.js, provide the essential infrastructure for handling HTTP requests and database interactions. The ultimate aim of CareerConnector is to create a dynamic, user-centric job portal that effectively bridges the gap between job seekers and employers. By streamlining the job search and recruitment process, it contributes to a more efficient and vibrant

Information Technology for Maintaining Records of the It Company Facilities

Accounting of premises is the process of storing, processing and manipulating accounting data of premises. In IT companies, effective premises accounting is of particular importance: it can help reduce costs, improve staff performance, and increase business efficiency. The need to keep records of the premises of an IT company always arises in the case of many offices or many manipulations of their data. Optimal use of space is one of the key factors in ensuring the efficiency of IT companies. The convenience of operational access to all the relevant information on the premises already greatly simplifies the conduct of business for IT companies. The availability of information technology with an analysis and decision- making system reduces the complexity of some accounting processes to a few mouse clicks by one of the relevant managers of the company. Study object – is the process of accounting for premises in an IT company, including the processes of collecting, processing, analysing, and displaying information necessary for efficient use of space. Scope of research - is the information technology for maintaining records of the IT company facilities, which allows to use of the space as efficiently as possible while minimizing costs. The goal this of research is to develop an IT company’s premises accounting information technology that will serve not only as a place to store and review accounting data but also as a platform for reserving places or premises and analysing their condition in accordance with regulatory documents and laws of Ukraine. The development of information technology for the accounting of premises for IT companies is a key component for solving the problem of efficient use of space and reducing costs. The developed technology allows collecting, processing, and analysing information on the use of premises in real-time, which will ensure effective control over the use of space and allow timely response to changes in requirements. This technology should be considered as a decision-making system, which not only manipulates data but also helps analyse and select information according to the user’s relevant requests. Among the important features and functions of the premise accounting technology can be attributed: to the creation, editing and deletion of premises from the system; analysis of the condition of the premises; access to all accounting and analytical data using a chatbot, which is built using an artificial intelligence system; booking a place or room for any amount of time during the day; review of current reservations of each room: review of all created reservations of a specific user; manipulation of employee data: registration, editing and deletion of users from the system, which can only be done by the manager.

PROMOTING COMPANYS „X” PRODUCT ON „FACEBOOK” PLATFORM

At a time when the popularity of social networks has greatly outgrown traditional media resources and the number of users is steadily increasing, companies have shown a number of new ways of reaching customers and informing about their products or services.
 The main advantages of social networks in business are their speed and simplicity. In addition, social networks, such as „Facebook”, collect a lot of information about users at user registration on social network, which makes it easier for companies to search for their potential customer. Apart from the resources, it is possible to promote product on „Facebook” social network in an economic way without actually spending money. It is important for companies to know all „Facebook” network possibilities of promoting product without big investments.

Design, development and evaluation of a mobile-based self-care application for patients with COVID-19 not requiring hospitalization; a study of Northwest of Iran

BackgroundGiven the effective role of a mobile applications in disease management, disease monitoring, and self-care in patients with COVID-19 disease, we aimed to design, development and evaluation of a self-care Mobile app for COVID-19 patients not requiring hospitalization.MethodsThe design, development and evaluation the usability of the self-care and education mobile app for patients with COVID-19 disease were conducted in two main phases at 2021 in Northwest of IRAN; (1) Determine the features and capabilities and (2) Design, development and evaluation of self-care mobile App. JAVA programming languages and Android Operating System were used and selected to design and development of a mobile app. There were 25 participants who conducted evaluations of the mobile app’s usability and impact using the mobile health app usability a Questionnaire of User Interface Satisfaction was administered to assess the usability of the developed application. The results were analyzed via Excel 2013.ResultsThe model of developing a mobile app as an Information System was the Waterfall model. The smartphone application based on a set of capabilities and features was designed and consists of two main parts: the login screen for user registration, and the main home menu. The user interface includes three main pages or activities; (a) Main Menu for quick access to all of the pages, (b) Symptom management and monitoring to monitor the signs and symptoms during the illness, and (c) Set Reminders and Alarms to notify patients. The users’ mean score of the application usability was calculated as 7.91 out of 9 indicating a good level of satisfaction.ConclusionThis app can be a guideline and a useful tools for managing and monitoring symptoms, reminding medications, and implementing self-care instructions in outpatients. The authors suggest evaluating the efficacy and functionality test of mobile-based applications for COVID-19 in clinical trial studies.

Authentication in Peer-to-Peer Cloud using AES and SRP

The system focuses on proposing a robust authentication framework based on these Secure Remote Password (SRP) protocol to enhance the security of data exchange in Peer-to- Peer (P2P) cloud environments. The Advanced Encryption Standard (AES) algorithm provides encryption and the SRP protocol leverages cryptographic primitives to establish mutual authentication between cloud servers and users, ensures that data, remains protected against any vulnerabilities. The system encompasses various stages, including user registration, key establishment, and secure data transmission. After user registration, the SRP protocol employs a zero-knowledge proof mechanism to maintain the security of data available on cloud servers, mitigating the risks associated with password leaks and unauthorized access. Furthermore, the protocol facilitates secure key exchange to establish a confidential communication channel between peers, enabling encrypted data transmission. The system integrates SRP protocol and AES encryption, fortifying data security in P2P cloud environments through robust authentication, ensuring a comprehensive defence against potential vulnerabilities.

Performance Analysis of Family Welfare Empowerment Application: A Kanban Method Approach

This research examines the application of the Kanban method in testing a family welfare empowerment application. The Kanban method, initially developed by Toyota in manufacturing, has been effectively applied in software development. This study involves a series of tests involving various features within the application, such as user registration, village data collection, processing of the family welfare empowerment data at the Village/District level, and more. The test results show that most tests were successful, highlighting the application's success in executing essential functions such as user registration and event scheduling. However, some tests failed, primarily in inputting village, hamlet, and community unit data. These results indicate that using the Kanban method in testing a family welfare empowerment application can potentially enhance development and testing efficiency. Metrics such as testing time, test success, and time efficiency have provided valuable insights into the application's performance. In conclusion, this testing provides a foundation for further application development, focusing on improving the areas that experienced testing failures. This research also opens up opportunities for further studies on using the Kanban method in software testing in various other application development contexts.

An Alternative to Vending Machines

In this review article for a smart vending refrigerator, the contours of the future device are thought out and outlined and all its advantages are described. This device will be controlled using Computer Vision and some other features. The main control unit will be Raspberry PI, since it is the best for this device. Also, a web application was developed in which the user registers, and the applica- tion itself transmits the user's information through an API that will be developed to communicate with the web server, and the web server will store this information. This article will analyze the systems that have been already on the market and their pros and cons, as well as consider the design, implementation and functionality of a smart vend- ing refrigerator. Also, the paper will consider key requirements for this system, technologies used, and approaches to integration with existing infrastructures. This design plays an important role in providing comfort and productivity in various fields of activity and can be applied in many areas.

Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods

Scheduling electric vehicle charging sessions allows to aggregate flexibility in order to minimize energy costs and reduce congestion in the electricity grid. Existing research shows that user input for energy and parking duration does not serve as a reliable prediction. The study presents an evaluation of the forecast error and computational performance of the different models. Two main methods are investigated: tree-based (gradient boosted trees LightGBM) and cluster-based (Gaussian mixture model). We also present a novel dynamic cluster-based method, the Similar Sessions method, which employs the similarity between charging sessions based on numerical variables. The results highlight the importance of selecting the forecast model influenced by the availability of training data. The effect of user registration on the accuracy of the forecast is investigated. The tests are run using ACN-Data dataset of Electric Vehicle charging sessions in California, United States. While underperforming on a small dataset with a short look-back period, tree-based methods show superiority while the charging data are accumulating. The Similar Sessions method shows superior accuracy under various data availability conditions. The proposed method requires no prior training, but has slower computational performance in deployment.

Secure Land Registration

Land Registration is very long and troublesome process in many parts of the world. There are various intermediate agents or agencies involved in this process. This paper takes a good look to the possibilities and the problems that will be solved using Block-chain based solution. The system is aEthereum based Blockchain system that stores all the transactions made during this Land- Ownership Transfer . We will use the concept of Contracts in Blockchain . In this Development various events such as User Registration , Uploading documents and fund transfer from buyer to seller will occur . The Proposed system solves the problem faced by all the three parties i.e. Buyer , seller and Government Officers. The system will also reduce the Frauds and the transactions will be Immutable due to blockchain. Keywords—component, formatting, style, styling, insert.

PT-SSIM: A Proactive, Trustworthy Self-Sovereign Identity Management System

Digital Identity Management (DIM) systems have become challenging, especially given the current progression in ubiquitous environments enclosing cooperative Internet of Things (IoT) devices, individuals, and organizations. Self-Sovereign Identity (SSI) has recently surfaced to manifest the notion of decentralized DIMs enlivened by users autonomy. This paper presents a proactive, trustworthy solution for managing digital users data and identity information without risking its integrity, security, privacy, and confidentiality. Accordingly, we propose a decentralized SSI-enabled cloud storage model that enables secure access control and frequent data integrity checking mechanism. The presented model ensures the resiliency of SSI-enabled operations, including users registration, data identification, external information distribution, integrity checking operations, identity information authentication and verification, and decentralized external and shareable operations.

The Online Identity Construction of the Elderly Women— Take Chinese Discourse Marker “Ah” as an Example

Recently, the registration of the elder user in cyberspace roses repeatedly. More and more old people prefer to realize self-expression through the Internet, among which the elderly woman is the majority. Based on self-constructed corpus, this paper examines the pragmatic functions of the Chinese discourse marker “Ah”, and discusses what kinds of pragmatic identity has been constructed. This paper discovers that, on the one hand, discourse marker “Ah” realizes topic organization function and interpersonal function; on the other hand, “ah” assists the elderly in constructing female identity, expert identity, elder identity and peer identity, and sometimes a compound identity of real and virtual identities so as to narrow the psychological distance with the audience and achieve the communicative purpose.