Categories Of Users Research Articles

Detecting Credit Card Fraud through Data Mining

The credit card industry grapples with a significant challenge in the form of credit card fraud, necessitating a comprehensive understanding of the various types of fraudulent activities and the evaluation of techniques deployed to detect such activities. Diverse strategies are employed by credit card firms and financial institutions, including banks, to prevent fraud, tailored to address the range of illicit behaviours encountered. The overarching goal of these efforts is to reduce instances of credit card fraud. However, persistent issues with current methodologies can lead to the erroneous classification of legitimate credit card users as fraudulent. Detecting credit card fraud through data mining entails analysing extensive datasets to identify patterns, anomalies, and trends indicative of fraudulent activities. Consequently, when employing data mining in credit card fraud detection systems, the outcome typically involves the recognition and flagging of transactions as potentially fraudulent or valid, contributing to enhanced fraud prevention measures.

User tendency-based rating scaling in online trading networks.

Social networks often involve the users rating each other based on their beliefs, abilities, and other characteristics. This is particularly common in e-commerce platforms where buyers rate sellers based on their trustworthiness. However, the rating tends to vary between users due to differences in their individual scoring criteria. For example, in a transaction network, a positive user may give a high rating unless the transaction was unsatisfactory while a neutral user may give a mid-rating, consequently giving the same numeric score to different levels of satisfaction. In this paper, we propose a novel method called user tendency-based rating scaling, which adjusts the current rating (its score) based on the pattern of past ratings. We investigate whether this rating scaling method can classify between "good users" and "bad users" in online trade social networks better when compared with using the original rating scores without scaling. Classifying between good users and bad users is especially important for anonymous rating networks like Bitcoin transaction networks, where users' reputations must be recorded to preclude fraudulent and risky users. We evaluate the proposed rating scaling method by performing user classification, link prediction, and clustering tasks, using three real-world online rating network datasets. We use both the original ratings and the scaled ratings as weights of graphs and use a weighted graph embedding method to find node representations that reflect users' positive and negative information. The experimental results showed that using the proposed rating scaling method outperformed using the original (i.e., unscaled) ratings by up to 17% in classification accuracy, and by up to 2.5% in link prediction based on the AUC ROC measure, and by up to 21% in the clustering tasks based on the Dunn-index.

Individual differences in service quality of university libraries in Bangladesh

This study measured the individual differences in the service quality of Bangladeshi university libraries by type of university, user and gender. A modified SERVQUAL (service quality) model was adopted for the academic library environment, which included 30 items under five dimensions with a 7-point Likert scale. The survey was conducted among 61 teachers, 50 researchers and 455 students from two public, two international and two private universities in Bangladesh. The individual differences in library service quality were calculated through an analysis of variance and independent sample t-test for the university, user and gender categories. The overall service quality was significantly different ( p < .001) among the users of the public ( M = 4.02), private ( M = 5.08) and international ( M = 5.10) university libraries, as well as between male ( M = 4.48) and female ( M = 5.01) users. Several statistical methods supported the sampling adequacy, reliability and validity of the adapted model, which could be used as a basis for further assessments of library service quality by different demographic groups, both locally and globally.

Examining the dynamic engagement process of passengers in online car-hailing system: a view of user value

ABSTRACT The online car-hailing system developed rapidly in recent years. This paper introduces the concept of user value and develops a machine learning framework for modeling the dynamic transformation of passenger value in online car-hailing system. Specifically, during the engagement process, four user value types representing the engagement status were captured based on both consumption and travel attributes. Additionally, we modeled user value conversion prediction throughout the engagement process, seamlessly integrating the benefits of features enhancement and time series prediction. Experiments using real order data demonstrated that the proposed machine learning framework has higher predictive accuracy than other methods. The value categories of online car-hailing users change over time. High-value users have high retention rates, while low-value users are the opposite, and medium-value users have the greatest potential. The results provide an important basis for modeling the engagement process of online car-hailing passengers, and for promoting the customer maintenance and management in online car-hailing systems.

Energy community with shared photovoltaic and storage systems: influence of power demand in cost optimization

AbstractEnergy management of distributed energy resources has gradually become a complex problem because of the intermittent nature of renewable energy sources, such as photovoltaic power, and the large use of energy storage systems. A way to deal with these issues is to operate within an energy community. However, the efficient management of the community in terms of costs is particularly relevant. Specifically, the minimization of the energy community costs, which consists of properly utilizing shared energy storage and renewable energy sources, becomes an important objective. In this context, a fundamental role is played by demand power characteristics which strongly influence the benefits brought by this energy management scheme. This work investigates the influence of the variability of power demand on the minimization of the operating cost problem of an energy community while determining the optimal capacity of the energy storage system that increases the self‐consumption potential of the photovoltaic source. Two main scenarios are implemented where the effects of considering the community photovoltaic capacity as a variable or a parameter on costs and energy storage system size are investigated. This analysis consists of a multi‐objective optimization coupled with a Monte Carlo framework. The community management is conducted by considering random power demand profiles of each unit belonging to the same community, and different sizes, categories of users and users' aggregations. A comparison is led among different users' categories in terms of costs, photovoltaic unit and energy storage system size. The results provide an overview of how each category benefits from taking part in an energy community both in terms of cost and energy storage and photovoltaic sizes and show how these aspects change within a multi‐category aggregation where each category makes a different contribution to the community. In particular, we find evidence of the “synergy effect” brought by multi‐category aggregations capable of exploiting differences in consumption profiles. Each building category, with its numerosity, has a different effect on the energy community, resulting in a different impact on total costs and cost savings. We also investigate how the energy storage system capacity is affected by both the available photovoltaic capacity and the consumption profiles of the categories within the energy community.

Techno-Economic and Sustainable Challenges for EV Adoption in India: Analysis of the Impact of EV Usage Patterns and Policy Recommendations for Facilitating Seamless Integration

This paper explores the intricate challenges that are impeding the widespread adoption of Electric Vehicles (EVs) and explores the concerted efforts of the research community towards addressing these obstacles. The surge in interest surrounding EVs as a sustainable transportation alternative is undeniable, yet several hurdles persist in hindering their mass acceptance. From limitations in battery technology and charging infrastructure to concerns over range anxiety and manufacturing sustainability, these challenges form a multifaceted barrier. However, the research community has been actively engaged in tackling each issue with innovative solutions. Advancements in battery chemistry and energy storage, coupled with improvements in charging networks and smart grid integration, are poised to reshape the EV landscape. Moreover, studies on user behavior, public policy, and lifecycle analysis are contributing to the development of holistic strategies for enhancing EV adoption. By delving into these challenges and the ongoing research endeavors, this paper sheds light on the evolving pathway towards a future where EVs can thrive as a mainstream mode of transportation. Also, an analysis is conducted to evaluate the economic viability of EVs based on daily range considerations, with the objective of determining which category of users would benefit most from adopting EVs. Furthermore, policies are proposed that are aimed at establishing a harmonious and balanced EV ecosystem.

Principles of Algorithmic Management

Our title harks back to Frederick W. Taylor’s influential book. But our identification of the principles of algorithmic management is certainly not advocacy. Our task in this essay is to develop a theory of algorithmic management in relation to fundamental changes in the shape and structure of organization in the 21st century that are reconfiguring boundaries, roles, and relations among managers, workers, engineers, professionals, consumers, and other user categories. In particular, such a theory must be attentive to transformations in the topology of organization. Today, many of the most valuable actors, assets, and activities are not located within the firm but involve a complex entanglement of information flows, practices, and users. This topology gives rise to a distinctive challenge: how to manage when the most valuable assets and activities are not in the firm. Whereas actors in hierarchies command, in markets they contract, and in networks collaborate, on platforms they are co-opted. The co-optation of actors, assets, and activities is undertaken by algorithmic management. To grasp the distinctive principles of algorithmic management, we compare and contrast the features of its ideology and practices with those of scientific management and the more recent collaborative management. Algorithmic management, we argue, operates within a different organizational form, articulates a different ideology, and addresses different managerial problems with different governance principles along different lines of accountability.

Anthropogenic-induced ecological risks on marine ecosystems indicated by characterizing emerging pollutants in Pearl River Estuary, China

Anthropogenic Contaminants of Emerging Concern (CECs) in marine environments have raised significant concerns. Yet, analyses detailing their origins, fate, and environmental effects are limited. This study employs an integrated non-target screening methodology to elucidate CECs existence across 46 sampling sites in the Pearl River Estuary (PRE) of the South China Sea. Assisted by advanced liquid chromatography-high resolution mass spectrometry, we discovered 208 chemicals in six usage categories, with pesticides (33 %) and pharmaceuticals (29 %) predominating. Several CECs drew attention for their consistent detections, profound abundance, and significant ecotoxicities. The wide detection of them at offshore sites further implies that anthropogenic activities may contribute to large-scale contamination. Meanwhile, distinct distribution patterns of CECs across PRE are evident in semi-quantitative results, indicating regional anthropogenic influences. Identified transformation products may establish a novel and non-negligible negative contribution to ecology through elevated environmental toxicities, exemplified by HMMM and atrazine. Based on the ecological risks, we compiled a prioritized list of 21 CECs warranting intensified scrutiny. Our findings indicate the introduction of various CECs into the South China Sea via PRE, emphasizing the urgent necessity for ongoing surveillance of discharged CECs at estuary areas and assessment of their marine ecological consequences.

Protection of children’s personal data in social networks in the context of the right to be forgotten

The article is devoted to a legal analysis of the protection of the confidentiality of children’s personal data on social networks in the context of the right to be forgotten and the General Data Protection Regulation. It is established that most social networks have their own usage rules, but their priority is to attract more users and company profit rather than the security of users’ personal data. Legal mechanisms for the implementation and protection of human rights in the digital environment are in the process of formation and improvement. It is proven that the application of the right to be forgotten has become one of the forms of personal data protection in social networks and on the Internet in general. Although initially the right to erasure emerged only at the level of the judicial practice of the Court of Justice of the EU, an effective mechanism has now been created to protect the individual’s right to delete outdated information about themselves, taking into account the balance between the right to privacy and the right to freedom of expression. Due to psychological immaturity and limited life experience, children and adolescents may casually disclose sensitive personal information, increasing the likelihood of abuse by both social networks and third parties. Therefore, minors receive special protection as vulnerable user categories according to the General Data Protection Regulation. It is argued that the incorporation of the right to be forgotten into EU legislation helps eliminate undesirable consequences of oversharing and restores minors’ ability to control their digital footprint. The terms of service of most social networks ensure additional protection of minors’ data and provide several options for deleting already disseminated information. Obstacles to the application of the right to be forgotten in social networks are identified, such as the lack of a developed mechanism for deleting photos or videos with minors published without the consent of the child or their parents by another user, the limited ability of users under 13 years old to request deletion without intermediaries, and the limited liability of social networks for the loss of information, data, and direct or indirect damages from such actions.

MCN4Rec: Multi-level Collaborative Neural Network for Next Location Recommendation

Next location recommendation plays an important role in various location-based services, yielding great value for both users and service providers. Existing methods usually model temporal dependencies with explicit time intervals or learn representation from customized point of interest (POI) graphs with rich context information to capture the sequential patterns among POIs. However, this problem is perceptibly complex, because various factors, e.g., users’ preferences, spatial locations, time contexts, activity category semantics, and temporal relations, need to be considered together, while most studies lack sufficient consideration of the collaborative signals. Toward this goal, we propose a novel M ulti-Level C ollaborative Neural N etwork for next location Rec ommendation (MCN4Rec). Specifically, we design a multi-level view representation learning with level-wise contrastive learning to collaboratively learn representation from local and global perspectives to capture complex heterogeneous relationships among user, POI, time, and activity categories. Then, a causal encoder-decoder is applied to the learned representations of check-in sequences to recommend the next location. Extensive experiments on four real-world check-in mobility datasets demonstrate that our model significantly outperforms the existing state-of-the-art baselines for the next location recommendation. Ablation study further validates the benefits of the collaboration of the designed sub-modules. The source code is available at https://github.com/quai-mengxiang/MCN4Rec .

Measuring user influence in real-time on twitter using behavioural features

Identifying influential users in online social networks, specifically Twitter, has drawn considerable research interest due to their profound influence on real-world events. Existing studies have primarily focused on topological aspects to identify influential users, which necessitate a complete network structure and ignore the behavioural aspects of the users. However, real-world network structure can be challenging to obtain. Thus, this study presents the BehaviourRank Influence (BRI) method, which focuses on evaluating Twitter user influence by analysing behavioural characteristics instead of traditional network structures. This approach acquires data in real-time to derive six behavioural characteristics by employing new metrics to achieve accurate and uniform characterizations. The proposed metrics are compared to baseline metrics from the literature using a user classification prediction model to validate their predictive ability. It also ranked behavioural characteristics by importance using SHAP and Information Gain approaches. To determine overall influence, a unique Rank-based Exponential Smoothing algorithm is proposed. The findings indicate that most features incorporating proposed measures exhibited superior performance compared to features with baseline metrics across all three datasets. The proposed feature sets achieved accuracy scores of 86.67%, 73.47%, and 85.87% in the respective datasets, while the baselined feature sets attained scores of 64.23%, 63.30%, and 66.30%. Moreover, the Rank-Based influence measurements algorithm provides a detailed and adaptable structure for measuring influence, recognising the significance of feature ranks in capturing the evolving nature of user behaviour.

Inaccurately Reported Statin Use Affects the Assessing of Lipid Profile Measures and Their Association with Coronary Artery Disease Risk.

Lipid profiling is central for coronary artery disease (CAD) risk assessment. Nonadherence or unreported use of lipid-lowering drugs, particularly statins, can significantly complicate the association between lipid profile measures and CAD clinical outcomes. By combining medication history evaluation with statin analysis in plasma, we determined the effects of inaccurately reported statin use on lipid profile measures and their association with CAD risk. We compared medication history of statin use with statin concentration measurements, by liquid chromatography-tandem mass spectrometry, in 690 participants undergoing coronary angiography (63 ± 11 years of age). Nominal logistic regression was employed to model CAD diagnosis with statin measurements, phenotypic, and lipid profile characteristics. Medication history of statin use was confirmed by statin assay for 81% of the patients. Surprisingly, statins were detected in 46% of patients without statin use records. Nonreported statin use was disproportionately higher among older participants. Stratifying samples by statin history resulted in underestimated LDL-lipid measures. Apolipoprotein B concentrations had a significant inverse CAD association, which became nonsignificant upon re-stratification using the statin assay data. Our study uncovered prominent discrepancies between medication records and actual statin use measured by mass spectrometry. We showed that inaccurate statin use assessments may lead to overestimation and underestimation of LDL levels in statin user and nonuser categories, exaggerating the reverse epidemiology association between LDL levels and CAD diagnosis. Combining medication history and quantitative statin assay data can significantly improve the design, analysis, and interpretation of clinical and epidemiological studies.

Simplistic Graphic User Interface Design for Rendering Blockchain Data (A Case Study of Educational Administrative Domain)

Blockchain data is characterized by encryption which is usually aimed at securing the data itself from getting into wrong hands. However, this limits the readability of the data when there is a need to. Clarity and understanding are essential indicators of a usable system as a complex but functioning system is of no value to the user if the data are not presented in a simple readable format. A Graphic User Interface (GUI) with a simplistic design is presented to solve this problem by rendering only necessary blockchain data while considering supplementary details that are usually hidden. 5 blockchain scenarios are created for this purpose. The activities and potential data involved are clearly stated. The raw blockchain data on a high level are recorded in form of JavaScript Object Notification (JSON). Attention is given to supplementary details from the raw blockchain data while translating them into graphical form. Results were presented in three different forms (raw blockchain data, complex GUI, and the simplistic GUI) to different categories of users and their comments were compared and analyzed. Since human computer interaction is an essential factor in user’s readability, the GUI rendered in this work is an enhancement to the blockchain technology as seen in the result which shows the simplistic GUI as the best understood interface even by a novice among the three.

PERSONAL DATA PROTECTION POLICY: ENSURING EFFECTIVE IMPLEMENTATION OF DATA PRIVACY POLICIES IN PRIVATE HIGHER INSTITUTIONS

Faculty of Business and Management Sciences, Kolej Universiti Islam Perlis, Malaysia As digital technology develops globally, the protection of personal data in the commercial sector becomes extremely important. Cases of selling data to third parties are increasing, and the government is required to tighten provisions in personal protection laws. Therefore, the Personal Data Protection Act 2010 (Act 709) was passed by the Parliament of Malaysia for the purpose of regulating the processing of personal data and to prevent the misuse of the personal data in commercial transactions. The Act confers rights on Data Subjects in relation to the collection, use and or retention (processing) of their Personal Data, and places obligations on Data Users. The Personal Data Protection Order (Group Data Users) 2013 lists the categories of data users who need to register with Personal Data Protection Department. One of group data users is the education sector including educational institutions and private higher education registered under the Private Higher Education Institutions Act 1996 (Act 555). As data users, private higher education institutions are responsible for protecting Personal Data of two categories of data subjects, namely students and employees in the institution. The obligation including to exercise an appropriate policies and procedures to safeguard the Personal Data collected, to maintain data accuracy, and to prevent unauthorized disclosure. This paper aims to measure the implementation of personal data protection laws including the formulation of operational policies and procedures in private higher education institutions. This will be done by looking at the provisions from the Act regarding the principles of data protection as well as the duties for compliance to the legislation. Furthermore, non-compliance of the Act commits an offence and on conviction, be liable to a fine not exceeding RM500,000 or to imprisonment for a term not exceeding 3 years or both. The study also seeks to determine what are the challenges in implementing the Act successfully.

Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO

Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios.

Digital communications of a modern library: key aspects

The article is devoted to the urgent problem of determining the key aspects of digital communications of a modern library when libraries create their own electronic information resources, products and services in order to quickly, meaningfully and qualitatively provide the information needs of modern users in the context of the digitalization of the city and region. The study of key aspects of digital communications of a modern library is relevant, since the main task of libraries in traditional and digital spaces is, first of all, information support for information, scientific, and research activities through the acquisition of a high-quality library collection, the formation and development of a system of electronic information resources, products and services, providing access to him, as well as fully ensuring the information needs of all categories of users. The fulfillment of these tasks occurs with the gradual digitalization of the region, the introduction of the latest computer technologies and telecommunications, which become the most important strategic resource and ensure the use of library and information potential in the scientific, cultural and educational spheres. Based on the study of professional publications, the definitions of the terms "digitalization" and "digital (digital) communications" are analyzed. The types of digital communications of a library institution are determined. Digital communications on the websites of libraries in the city of Dnepr were studied, depending on their types and purpose. The role of library digital communications in the creation of library electronic resources, services and providing the information needs of remote users is indicated. The key aspects of digital communications of a modern library are identified. Measures are proposed for the organization and further development of digital communications of library institutions, depending on their types and main mission.

Sustainability and Corporate Reporting using ERP Systems: Challenges and Opportunities for the Accounting and Auditing Profession

As a result of the massive digitalization of recent years, ERP systems have started to play an important role in the life cycle of an organization, raising a number of challenges and opportunities especially for the accounting and auditing profession. The accounting and the auditing professions represent two very versatile professions as a result of the changes that have taken place from the moment of their appearance until now, they are constantly improving with the evolution of digitalization. Digitalization has led to the development of accounting and auditing organizations and of course to the focus of attention on sustainability processes and how their corporate reporting is done. The many advantages that ERP systems offer to professional accountants and auditors, as well as other categories of users, have influenced many organizations to resort to the implementation of these systems. Thus, to highlight the main challenges and opportunities that ERP systems offer to the accounting and auditing profession from the perspective of how to ensure sustainability and corporate reporting, the authors performed a quantitative bibliometric analysis, selecting a sample of Web of Science cited articles. The results indicated that ERP systems have a major impact on the decisions adopted by organizations, more specifically they ensure a true picture of the organization's situation, having a significant influence on the way of corporate reporting and on sustainability.

Big Data Anomaly Prediction Algorithm of Smart City Power Internet of Things Based on Parallel Random Forest

Abstract Conventional power data prediction algorithms easily lead to the loss of key power data in a complex wireless network environment. Therefore, a power big data anomaly prediction algorithm based on parallel random forest is proposed. According to the power big data anomaly prediction algorithm based on parallel random forest, a network power big data anomaly prediction algorithm platform is established, and based on the platform, key data features such as user address and power transmission packet structure are extracted according to the category of power users. According to the relationship between power shunt function value and power data unit density, the parameter value of the system and finally the reasonable anomaly prediction of power big data in wireless network are determined. Finally, filter the classified data through attribute reduction and gene expression programming algorithm to obtain the data to be encrypted and complete the research on the anomaly prediction algorithm of power big data. Experimental results show that the proposed algorithm has better prediction performance and can ensure better data prediction effect.

Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning

This study introduces a novel tool for classifying user needs in user experience (UX) design, specifically tailored for beginners, with potential applications in education. The tool employs the Kano model, text analysis, and deep learning to classify user needs efficiently into four categories. The data for the study were collected through interviews and web crawling, yielding 19 user needs from Generation Z users (born between 1995 and 2009) of LEGO toys (Billund, Denmark). These needs were then categorized into must-be, one-dimensional, attractive, and indifferent needs through a Kano-based questionnaire survey. A dataset of over 3000 online comments was created through preprocessing and annotating, which was used to train and evaluate seven deep learning models. The most effective model, the Recurrent Convolutional Neural Network (RCNN), was employed to develop a graphical text classification tool that accurately outputs the corresponding category and probability of user input text according to the Kano model. A usability test compared the tool’s performance to the traditional affinity diagram method. The tool outperformed the affinity diagram method in six dimensions and outperformed three qualities of the User Experience Questionnaire (UEQ), indicating a superior UX. The tool also demonstrated a lower perceived workload, as measured using the NASA Task Load Index (NASA-TLX), and received a positive Net Promoter Score (NPS) of 23 from the participants. These findings underscore the potential of this tool as a valuable educational resource in UX design courses. It offers students a more efficient and engaging and less burdensome learning experience while seamlessly integrating artificial intelligence into UX design education. This study provides UX design beginners with a practical and intuitive tool, facilitating a deeper understanding of user needs and innovative design strategies.

A Comparison of One- and Two-Handed Gesture User Interfaces in Virtual Reality—A Task-Based Approach

This paper presents two gesture-based user interfaces which were designed for a 3D design review in virtual reality (VR) with inspiration drawn from the shipbuilding industry’s need to streamline and make their processes more sustainable. The user interfaces, one focusing on single-hand (unimanual) gestures and the other focusing on dual-handed (bimanual) usage, are tested as a case study using 13 tasks. The unimanual approach attempts to provide a higher degree of flexibility, while the bimanual approach seeks to provide more control over the interaction. The interfaces were developed for the Meta Quest 2 VR headset using the Unity game engine. Hand-tracking (HT) is utilized due to potential usability benefits in comparison to standard controller-based user interfaces, which lack intuitiveness regarding the controls and can cause more strain. The user interfaces were tested with 25 test users, and the results indicate a preference toward the one-handed user interface with little variation in test user categories. Additionally, the testing order, which was counterbalanced, had a statistically significant impact on the preference and performance, indicating that learning novel interaction mechanisms requires an adjustment period for reliable results. VR sickness was also strongly experienced by a few users, and there were no signs that gesture controls would significantly alleviate it.