User Account Research Articles

Accessibility of Shopee Pay Later and Its Impact on Community Income in Malang City about Cash Swipe Behavior

The presence of e-commerce in the industrial world, which is currently supported by the pay later feature, has become a means of easy accessibility in carrying out buying and selling transactions. Funds in pay later should only be used to transact goods on e-commerce applications. However, many people abuse the presence of the pay later feature to disburse it into money which can increase the balance in the user's account. This activity is contrary to the pay later principle which is used as a transaction to buy goods, not exchange them for cash. The research method used is descriptive qualitative with an exploratory approach based on triangulation techniques based on primary and secondary data sources through observation, interviews and literature studies. This research aims to find out more about Shopee pay later accessibility and people's income in Malang City regarding cash swipe behavior. There are many advantages and disadvantages, both in terms of providers and users of gestun services. Apart from that, there are several obstacles that often occur in the transaction process. With this research, researchers hope that there will be further research regarding Shopee pay later gestures and can educate the wider community regarding the dangers and impacts that will arise from using Gestun services. Researchers also hope that there will be firmness from the authorities to follow up on gestun behavior in society.

Traversable Ledger for Responsible Data Sharing and Access Control in Health Research

Healthcare institutions and health registries often store patients’ health data. In order to ensure privacy, sensitive medical information is stored separately from the identifying information of the patient. Generally, institutions anonymize medical information while sharing it for external use. However, internal users may also use it for identifying inaccuracies or missing information. Even though internal users may be legally permitted to access sensitive medical information, such access may lead to the identification of the patient, which can be vulnerable to patient privacy. Ensuring the accountability and responsibility of the internal users may lead to tractability in case of adversarial access with malicious intentions. Therefore, a secure system must be developed for the storage and retrieval of health data. To this end, in this paper, we propose a ledger-based system that cryptographically ensures that all access to health data must be logged into a ledger. Nevertheless, the ledger entries must be protected against adversarial access, too. At the same time, the ledger must be traversable by the patients as well as internal users. To address these needs, we propose techniques for the construction of a ledger to permit both internal users and patients to securely traverse and view only the entries to which they are linked.

Zero Trust Security Model for Enterprise Networks

Zero Trust (ZT) is security model and follow the concept of ânever trust, always verifyâ. ZT require to strict identity verification for devise and clients trying to access recourses on private networks regardless of whether they are sitting within or outside the networks. As opposed to perimeter-based architecture, which makes the assumption that all internal network parties are trusted and all external network parties are untrusted. In enterprise network the internal network parties is automatically seen as trusted entities granting them access to network resources. The insider threat actor has been successful in exploiting their access. So that, enterprise networks become more exposed to inside and outside threads. As a result, we need to add the zero-trust principle to the enterprise network to protect it from the inside. In this paper, the ZT model, is assumed inside the militarized zone. There may be a threat to the sensitive data. Any internal company network users cannot manipulation on his computer without permission from the administrator this is done by the group policies that have been implemented in ZT. This model has been shown to be quite effective in protecting the sensitive data against unauthorized access and also the manipulation by the insider user. Following that, an attack originating from inside of the network was launched against the enterprise and zero trust network. In the enterprise network, the network was effectively attacked, and the attack’s validity was also increased to fully penetrate the enterprise. The attack did not succeed in the zero-trust network because the attacker cannot pass the User Account Control (UAC) to gain the NT authority.

Public attitudes towards social media field experiments.

The growing use of social media field experiments demands a rethink of current research ethics in computational social science and psychological research. Here, we provide an exploratory empirical account of key user concerns and outline a number of critical discussions that need to take place to protect participants and help researchers to make use of the novel opportunities of digital data collection and field studies. Our primary contention is that we need to elicit public perceptions to devise more up-to-date guidelines for review boards whilst also allowing and encouraging researchers to arrive at more ethical individual study design choices themselves. To ground our discussion in real-world examples of online experiments, we focus on recent social media studies in the field of misinformation, polarization, and hate speech research. We conclude by discussing how we can better strike a balance between meeting ethical guidelines and the concerns of social media users alongside maximizing scientific impact and credibility.

A Novel Model Based on Ensemble Learning for Phishing Attack

With the increase in the speed of the internet environment and the development of the infrastructures used, people have started to perform most of their work online. As much as this makes life easier, it also increases the possibility of being attacked by malicious people. Attackers can activate a phishing attack that aims to steal information from victims by creating copied, fake websites. While this attack is very old and somewhat simple, it can still be effective due to low IT literacy. People can enter their information on these fake websites out of spontaneity or ignorance or good intentions and be exposed to Phishing attacks. The compromise of a user's account information also puts at risk the security of the organization or institution to which it is connected. In this study, we propose a new machine learning-based ensemble model with feature selection methods to detect phishing attacks. Also, an ablation study is presented to measure the effect of different feature selection methods. The proposed model which we named as NaiveStackingSymmetric (NSS) is analyzed using the widely used accuracy (ACC), the area under curve (AUC), and F-score metrics as well as the polygon area metric (PAM), and it is shown that it outperforms other studies in the literature using the same dataset.

Domesticating energy flexibility. Learning from great britain’s 2022–2023 demand flexibility service

Demand-response is essential for balancing electricity grids with increasing proportions of intermittent renewable sources of generation. Harnessing the potential for demand-response in domestic settings could deliver widespread benefits for electricity systems and householders. This study applies domestication theory to a major national domestic electricity demand-response programme in Great Britain, exploring how participants integrate demand-response sessions into their everyday routines. The study uses empirical data from twenty-five participants who completed diaries reflecting on their experience of taking part in thirteen demand-response sessions scheduled over a five-month period in winter 2022–23. The study identifies and analyses five pathways for domesticating demand-response, making recommendations to support system-actors in boosting and sustaining the adoption of demand-response. The study concludes that designing electricity demand-response programmes, as a staple of secure low-carbon energy systems, will need to take full account of users’ non-financial motivations, previous experiences of demand-response, and access to enabling technology.

Legislative and Ethical Foundations for Future Artificial Intelligence

The effective Regulation and Ethical of Artificial Intelligence is an urgent policy concern. Legislatures and regulators do not possess the specialized technical expertise necessary to effectively convert popular requests into legislative mandates. The excessive dependence on industry self-regulation results in a lack of accountability for AI ethical system and users in meeting democratic requirements. The concept of Ethical Frameworks involves governments mandating the entities subject to regulation to get regulatory assistance from a private regulator. This AI Ethical Frameworks has the potential to address the shortcomings of both command-and-control regulation and self-regulation. Most of advanced states provide governments the opportunity to set policy priorities for AI Ethical Frameworks, while using market forces and industry research and development (R&D) to develop the most effective ways of regulation to set up Ethical Frameworks that align with policymakers' goals.

It Is Time to Take Complaints Seriously? An Exploratory Analysis of Communications Sent by Users to a Public Healthcare Agency before, during and after the COVID-19 Pandemic

Taking due account of users’ perspectives is crucial for improving the quality of healthcare services. This study aimed to analyse the representations and evaluation criteria that users of a public health agency express towards care and treatment services and to explore whether and how the content and meaning of their communications varied according to pre-pandemic, pandemic or post-pandemic periods. A total of 501 communications sent to the public relations office of an Italian health agency were collected. An automatic content analysis procedure was applied to the textual corpus. Four main thematic cores were identified concerning the request for care and respect, the value of the doctor–patient relationship and the difficulties in contacting services and accessing care. Two main latent dimensions of meaning were identified, which capture the dialectic between the demand for relationships and the demand for access to care, and between attention to the relational competence of health workers and attention to the needs and rights of users. Communications collected during the pre-pandemic and post-pandemic periods mainly concern the difficulty of access to care; those collected during the pandemic period mainly concern the doctor–patient relationship. Interpersonal aspects and timely access to care appear to be crucial in users’ assessment of the quality of care.

A Comparative Study Between Two Cybersecurity Attacks: Brute Force and Dictionary Attacks

In today's world, information drives not only business but nearly every aspect of human life. Therefore, safeguarding valuable information from malicious activities like attacks has become essential. Brute force search and dictionary attacks are prevalent cybersecurity threats, in which an attacker systematically attempts all possible passwords and passphrases to gain access to a user's account. These types of attacks are common due to the widespread reuse of simple password variations. The goal of this study is to discover as many passwords as possible and demonstrate their predictability and susceptibility. Our focus was on comparing two cracking methods, such as brute force search attacks and dictionary attacks, assessing their effectiveness and time requirements.

Rfid Technology in Library and Information Science

The radiofrequency identification system (RFID) makes it possible for devices like computers or machines to automatically recognize things, record metadata, or control certain targets. A technique called Radio Frequency Identification (RFID) makes it possible to identify objects without a direct line of sight. For administering the automated library and detecting theft, libraries are using the most recent technologies. The inventory, issuance, and return status of books, gadgets, and other objects in the library can all be monitored with the help of RFID systems. Additionally, it ensures that your library is secure and that no priceless resources are stolen from it. The library management system receives the ID number from the RFID system and uses it to retrieve the book, check it out, and produce a receipt. When a visitor returns a book, the tag responds with the ID number, and the system credits the user's account. The automated identification of library documents involved in a certain technical process requires reading data from the tag memory located in the working region of the RFID reader. Radio-frequency labels of the same type as the library labels but not those can also fall within the reading zone at the same time. Libraries must make sure that their RFID systems adhere to all applicable laws, keep them updated often, and instruct their employees and visitors on how to use them effectively.

The role of suspicious accounts in setting political discourse: a study of the Pakistani Twitter space

PurposeThis paper aims to explore the use and impact of social media, specifically Twitter (now X), in political mobilization in Pakistan. It focuses on the events followed by the no-confidence motion against Imran Khan as Pakistan’s prime minister in April 2022 and the protest campaign that ensued, facilitated through the strategic use of the Urdu hashtag #امپورٹڈ_حکومت_نامنظور (translated as “imported-government unacceptable”) on Twitter, both within and outside Pakistan.Design/methodology/approachUsing Web scraping, data from Twitter was extracted and analyzed between 2022 and 2023. By probing into user account profiles and interactions with this hashtag, this paper investigates the claims surrounding the hashtag’s popularity, by identifying suspicious accounts and their contributions in the trending of the hashtag.FindingsFindings suggest that the claim of the hashtag's unprecedented success was overhyped, further suggesting that the popularity and impact of the social media campaign were exaggerated. Despite high engagement rates, the study indicates a discrepancy between perceived influence and actual impact on public sentiment and political mobilization.Originality/valueThis paper contributes to the literature on social media’s role in political mobilization and agenda-setting in the Pakistani context. More generally, understanding hashtag dynamics and their impact on shaping public opinion, may be beneficial to academics and practitioners in better understanding the role of digital platforms in the politics.

Digital identities in the metaverse: Privacy, security, and user authentication in virtual financial systems

The advent of the metaverse has ushered in a new era of digital interaction, commerce, and finance. As users increasingly immerse themselves in virtual spaces, the need for robust digital identities becomes paramount. This study investigates the intricate interplay of privacy, security, and user authentication within the context of digital identities in the metaverse, with a particular focus on virtual financial systems. Employing a cross-case analysis method within the qualitative research paradigm, the study extensively examines real-world instances to extract valuable insights, identify challenges, and propose potential solutions in navigating this dynamic landscape. This approach allows for a comprehensive examination of overarching patterns and trends across case studies, revealing persistent challenges in digital identity management. The study delves into the complexities of pseudonymity in virtual engagement, emphasizing the crucial balance required for user trust and accountability. Furthermore, it uncovers commonalities in successful implementations, spotlighting the transformative role of blockchain technology, exemplified by IBM Blockchain World Wire, and the growing trend of biometric authentication, as seen in the FIDO use case. The successful cases examined demonstrate that a user-friendly experience and scalability are integral to the adoption of advanced security technologies. Industry leaders should take note of the global trends in digital transactions, emphasizing efficiency, transparency and cross-border security. Additionally, the study offers forward-looking insights with implications for future research and practice, advocating for continued exploration of the nuanced dynamics between user privacy, trust-building mechanisms and accountability in the ever-evolving metaverse landscape.

Realization of Reliable and Effective Authentication in Intelligent Systems by Using Visual Biometrics Methods

The main purpose of this article is to consider the aspects of ensuring security and increasing the efficiency of the authentication process in intelligent systems using visual biometrics. The work is aimed at the development and improvement of authentication systems using advanced biometric identification methods. An intelligent system has been created that ensures secure authentication of users of the current system, using a Siamese neural network. In addition to the implementation of basic security measures in the form of hashing and saving user logins and passwords, the implementation of two-factor authentication is important nowadays, which significantly strengthens the protection of user data and prevents most modern methods of hacking and stealing user data. Two-factor authentication is implemented as a technology for searching, recognizing and comparing the faces of system users, as visual biometrics is more secure than other types of two-factor authentication. Different variations of the possible implementation of Siamese neural network using Contrastive loss function and more modern Triplet loss function were reviewed and accordingly, a neural network using Triplet loss function was accomplished and trained. After training and verifying the correct operation of the neural network, it was integrated into the created intelligent system, thanks to which an effective way of recognizing the face of the system user was created, saving the received information in the database and further comparing the current user with the stored face during authentication. As a result, a secure and reliable intelligent system was created that cutting down the risk of unapproved access to the user account and uses an effective and modern method of user authentication.

Easy Electricity Billing System

Due to various issues, electricity users frequently deal with the issue of inaccurate and delayed monthly billing. For these reasons, having an effective system via an electronic platform that takes proximity into account is crucial. The laborious and time-consuming process of visiting the Electricity Board to pay an electricity bill is automated by the suggested method. For user convenience, it is also intended to automate the computation and payment of electricity bills. Java Swings, a foundational programming language for creating websites, online apps, and web services, is utilized in the development of the system. Back-end databases are also created using the Microsoft Structured Query Language (SQL) server. The administrator and user logins would be the two logins available on the system. The administrator can upload the customer's details regarding the energy units they consumed this month to their account and check the user's account data. Data about electricity usage must be entered into each user's account by the administrator. Next, each user's electricity cost is computed by the system, which then updates the data in their account each month. Once viewed, users can pay their electricity bill before the end of the month

Research Project Topic Recommender System Using Generative Language Model

Education has become a driver of a person's continuous innovation to improve their quality. Currently, the use of artificial intelligence determines progress in education. In this research, artificial intelligence technology was applied to develop a web-based recommendation system to help students at the Faculty of Computer Science, Klabat University, choose appropriate research topics for their final assignments. To provide personalized and contextually relevant suggestions, the recommendation system leverages deep learning and generative language models, specifically GPT-3. The Rapid Application Development process model is employed to develop the system. Its key components include semantic search, rapid engineering, and an advanced vector database for effective data management and retrieval. The functions provided by the system include user account registration, login, input of major subject grades and research preferences, and personalized recommendation results. Some additional features such as profile management, previous recommendation history, and password reset options are also provided. All these functions have been tested using the black box method.

Real Estate Website

Abstract— This paper presents a novel real estate management system designed to address the inefficiencies faced in the contemporary property market. Developed using PHP and MySQL, the system offers a user-friendly web application that streamlines the processes for both property seekers and managers. Catering to distinct user groups, it empowers users with a comprehensive property database searchable by location and desired features. Additionally, users can leverage a built-in loan calculator (depending on implementation) for financial planning and potentially express interest in suitable properties. Admins, on the other hand, benefit from comprehensive management functionalities. These include user account management, property listing creation, editing, and availability control. The system also offers the potential to manage real estate agents, further enhancing its functionality for larger organizations. This user-centric approach prioritizes a user-friendly interface that simplifies searching, listing management, and overall interaction with the system. To ensure data security and integrity, the system implements robust role-based access control, restricting access to sensitive information based on user roles. Furthermore, a powerful search function facilitates efficient property discovery, allowing users to find their ideal properties with minimal effort. This paper delves into the details of the system, outlining its functionalities, the chosen development methodology (e.g., Agile), and the software and hardware requirements for running the application. It also acknowledges the system’s limitations in its initial phase, such as the potential need for future expansion of search filters or communication tools. Finally, the paper explores the exciting future scope of the system, including advanced search capabilities, integrated communication features, mobile application development, and potential integration with smart home technologies and machine learning for personalized recommendations. Keywords – Real Estate Management System, Property Management System, PHP,MySQL,User-Friendly Interface

Efficient and accountable anti-leakage attribute-based encryption scheme for cloud storage

To ensure secure and flexible data sharing in cloud storage, attribute-based encryption (ABE) is introduced to meet the requirements of fine-grained access control and secure one-to-many data sharing. However, the computational burden imposed by attribute encryption renders it unsuitable for resource-constrained environments such as the Internet of Things (IoT) and edge computing. Furthermore, the issue of accountability for illegal keys is crucial, as authorized users may actively disclose or sell authorization keys for personal gain, and keys may also passively leak due to management negligence or hacking incidents. Additionally, since all authorization keys are generated by the attribute authorization center, there is a potential risk of unauthorized key forgery. In response to these challenges, this paper proposes an efficient and accountable leakage-resistant scheme based on attribute encryption. The scheme adopts more secure online/offline encryption mechanisms and cloud server-assisted decryption to alleviate the computational burden on resource-constrained devices. For illegal keys, the scheme supports accountability for both users and the authorization center, allowing the revocation of decryption privileges for malicious users. In the case of passively leaked keys, timely key updates and revocation of decryption capabilities for leaked keys are implemented. Finally, the paper provides selective security and accountability proofs for the scheme under standard models. Efficiency analysis and experimental results demonstrate that the proposed scheme enhances encryption/decryption efficiency, and the storage overhead for accountability is also extremely low.

CANTEEN MANAGEMENT SYSTEM : THE FINAL PROJECT

ABSTRACT : A sophisticated digital solution called the School Canteen Management System (SCMS) was created to streamline the management and operations of school canteens. Essential functions including menu planning, user account administration, inventory management, point of sale (POS), and extensive reporting and analytics are all included in this system. Through process simplification and automation of repetitive tasks, the SCMS increases productivity, lowers error rates, and enhances user experience. Faster service, precise financial tracking, improved stock control, and the capacity to satisfy dietary requirements and preferences are some of the main advantages. In the end, the SCMS ensures convenience and happiness for students, staff, and parents by helping to create a more orderly, transparent, and user-friendly cafeteria environment. INDEX TEARMS – Frontend development HTML, CSS, JavaScript, react js, Database management, mongodb, Chatgpt.

Algorithmic Content Recommendations on a Video-Sharing Platform Used by Children

Free video-sharing platforms (VSPs) make up a high proportion of children's daily screen use. Many VSPs make algorithmic recommendations, appearing as thumbnail images from the video, which content creators use to advertise their video content. To explore how VSP thumbnails use attention-capture designs to encourage engagement with content and to test whether VSP algorithmic recommendations offer more problematic thumbnail features over time. In this cross-sectional study conducted in January 2022, researchers mimicked the search behavior of children on a popular VSP by randomly clicking on recommended videos in order to test whether thumbnail designs changed over 20 sequential video engagements. A digital, footprint-free data collection setting was created by using a new computer and wireless internet router. Data were collected from YouTube via an internet browser not logged into a user account. Data analysis occurred from April to December 2022. Manual searches using 12 top-searched terms popular with school-aged children were conducted. Researchers captured the video thumbnails recommended at the end of each video and randomly clicked subsequent videos for 20 sequential engagements. Thumbnail content codes were developed through iterative review of screenshots by a multidisciplinary research team and applied by trained coders (reliability, κ >.70). The prevalence of problematic thumbnail content and change in prevalence over 20 engagements was calculated using the Cochran-Armitage trend test. A total of 2880 video thumbnails were analyzed and 6 features were coded, including visual loudness; drama and intrigue; lavish excess and wish fulfillment; creepy, bizarre, and disturbing; violence, peril, and pranks; and gender stereotypes. A high proportion contained problematic features including the creepy, bizarre, and disturbing feature (1283 thumbnails [44.6%]), violence, peril, and pranks feature (1170 thumbnails [40.6%]), and gender stereotypes feature (525 thumbnails [18.2%]). Other features included attention-capture designs such as the visual loudness feature (2278 thumbnails [79.1%]), drama and intrigue feature (2636 thumbnails [91.5%]) and lavish excess and wish fulfillment feature (1286 thumbnails [44.7%]). Contrary to the hypotheses, problematic feature prevalence did not increase over time, but the gender stereotypes feature increased with more engagement in the recommendations feed (P for trend < .001). In this study of video recommendations for search terms popular with children, thumbnails contained problematic and attention-capturing designs including violent, stereotyped, and frightening themes. Research is needed to understand how children respond to thumbnail designs and whether such designs influence the quality of content children consume.

Menganalisis Jaringan Sosial Penggemar Blackpink Saat Konser di Jakarta

The phenomenon of digital fandom behavior that often occurs on social media is always interesting to analyze. Social network analysis is becoming increasingly used to research this phenomenon. Social media plays an important role in providing accurate data for analysis. Twitter is the social media most used in conducting social network analysis. Based on data released (We Are Social, 2022), Twitter is the seventh favorite social media out of 16 social media platforms in the world. In Indonesia itself, according to data (dataindonesia.id, 2023), 24 million of the world's 556 million Twitter users are Indonesian. This research will analyze Black Pink as an extraordinary phenomenon with a fanatical fandom, ahead of their concert in Jakarta 10-11 March 2023. The aim of this research is to conduct a social network analysis specifically on the hashtag BORNPINKinJAKARTA, and the role of social network analysis for each Twitter user account in their relationships. This research uses Digital Fandom Theory with the Descriptive Analysis method with free Netlytics tools. This research found that the hashtag #BORNPINKInJAKARTA was the hashtag most frequently posted by Black Pink fans, which then strengthened the digital fandom theory