Personal Usage Research Articles

Perception is reality? Understanding user perceptions of chatbot-inferred versus self-reported personality traits

Artificial Intelligence (AI) can infer one's personality from online behavior, which offers an interesting alternative to traditional, self-reported personality assessments. Recent studies comparing AI-inferred personality to personality derived from traditional assessments have found noticeable differences between the two (meta-analyses have found mean correlations of 0.3 between AI-inferred personality and personality from surveys). One important but unanswered question is how users perceive their personality derived from both methods. Which do users perceive to be more accurate, and more satisfying to use? To answer this question, we used both methods to conduct personality assessments of 595 participants and then asked users how well the two sets of results fit them, as well as their satisfaction and intention to use them. Participants reported that both results fit them equally well, even though the two methods reported different personality scores. Users were equally satisfied with both methods but were more likely to use the survey, likely because it took less time. Our findings imply that both methods measure different aspects of user personality, and both may be useful. We discuss the pros and cons of AI-inferred versus traditional, self-reported personality and indicate future research directions of AI-inferred personality assessment and the implications of their use for real-world applications.

Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy.

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and ethics. This article presents a comprehensive survey of MU, covering current state-of-the-art techniques and approaches, including data deletion, perturbation, and model updates. In addition, commonly used metrics and datasets are presented. This article also highlights the challenges that need to be addressed, including attack sophistication, standardization, transferability, interpretability, training data, and resource constraints. The contributions of this article include discussions about the potential benefits of MU and its future directions. Additionally, this article emphasizes the need for researchers and practitioners to continue exploring and refining unlearning techniques to ensure that ML models can adapt to changing circumstances while maintaining user trust. The importance of unlearning is further highlighted in making artificial intelligence (AI) more trustworthy and transparent, especially with the growing importance of AI across various domains that involve large amounts of personal user data.

Predicting the usability of mobile applications using AI tools: the rise of large user interface models, opportunities, and challenges

This article proposes the so-called large user interface models (LUIMs) to enable the generation of user interfaces and prediction of usability using artificial intelligence in the context of mobile applications. To this end, we synergized an integrated framework for the effective testing of the usability of mobile applications following a selective review of the most influential models of mobile usability testing. Next, we identified and analysed 13 recent AI tools that generate user interfaces for mobile apps, and systematically tested these tools to identify their AI capabilities. Our striking findings demonstrate that current generative UI tools fail to address mobile usability attributes, such as efficiency, learnability, effectiveness, satisfaction, and memorability. Our large UI models’ architecture proposes to leverage the capabilities of large language models, large vision models, and large code models to overcome the challenges of AI-driven UI/UX design and front-end implementations. This fascinating UI eco-system must be augmented with sufficient UI data and multi-sensory input regarding user behaviour to train the models. We anticipate LUIMs to create ample opportunities, like expedited frontend software development, enhanced personalised user experience, and wider accessibility of smart technologies. However, the research challenges hindering the UI generation and usability prediction of mobile apps include the seamless integration of complex generative AI models, semantic understanding of non-uniform visual designs, scarcity of UX datasets, and modelling of realistic user interactions.

Speaker Identification in Crowd Speech Audio using Convolutional Neural Networks

Crowd speaker identification is the most advanced technology in the field of audio identification and personal user experience which researchers have extensively focused on, but still, science hasn’t been able to achieve high results in crowed identification. This work aims to design and implement a novel crowd speech identification method that can identify speakers in a multi speaker environment, (two, three, four and five speakers). This work will be implemented through two phases. The training phase is the Convolutional Neural Network (CNN) training and testing phase. Through this phase, the training will be implemented on data generated via the Combinatorial Cartesian Product approach. This approach uses two primary processes, the Computation of the Cartesian product process and combinatorial selection process. The second phase is the prediction phase. The aim of this phase is to check the CNN trained in the first phase, through testing it on new crowed audios than the data that the CNN was trained on in the first phase, these new crowded audios exist in the Ghadeer-Speech-Crowd-Corpus (GSCC) dataset, which is a new database designed through this work. Compared to the state-of-the-art speaker identification in multi speaker environment approaches, the results are impressive, with a recognition rate of 99.5% for audio with three speakers, 98.5% for music with four speakers, and 96.4% for audio with five speakers.

BiVAE를 활용한 MBTI 기반 OTT 서비스 개인화 추천 시스템

The OTT platform, which provides various media contents in the modern society, seeks to improve the user experience by utilizing a personalized recommendation system. However, current recommendation systems are having difficulty in making accurate recommendations because they do not fully understand user's tendencies. This study proposes the use of a personalized recommendation algorithm using BiVAE (bilateral variational autoencoder) to solve this problem, and considers group-specific applications through MBTI. BiVAE is a probabilistic generation model that compresses and generates data considering of bidirectional information flow, providing richer expression and reconstruction capabilities by encoding and decoding in both directions. As a result of applying four types of data to the model, when applying BiVAE for each group using MBTI, the accuracy of OTT genre recommendation was shown to be superior to that of other methods. In addition, when considering MBTI, a personal recommendation using BiVAE showed higher accuracy compared to the existing recommendation system. This result means that the personalized recommendation system considering the user's personal characteristics and personality is important.

Electronic Banking and Its Impact on Customer Satisfaction of Nepalese Commercial Banks

Electronic Banking and its impact on customer satisfaction of Nepalese commercial banks explores the relationship between internet banking and customer satisfaction. Internet banking allows customers to conduct real-time banking transactions electronically, offering convenience and accessibility. Utilizing a descriptive and casual comparative research design, the study examines internet banking users across 20 commercial banks in Nepal, focusing on Nepal Investment Bank Limited, Nabil Bank Limited, and Himalayan Bank Limited. Through purposive sampling, 150 respondents were selected, with 50 from each bank, for primary data collection. The findings indicate high levels of satisfaction with online banking services, with respondents expressing agreement on various aspects such as ease of website navigation, meeting personal needs, and prompt service delivery. Correlation analysis reveals positive and significant relationships between customer satisfaction and key factors including efficiency, personal need fulfillment, reliability, responsiveness, site organization, and user friendliness. Finding suggest that a strong association between internet banking and customer satisfaction. These findings underscore the importance of internet banking in enhancing customer experience and satisfaction within the Nepalese commercial banking sector. Electronic banking and its impact on customer satisfaction of Nepalese commercial banks.

Adoption of M-Commerce Amongst Pakistani Consumers: By Using TAM with Effect of Social Influence and Mobile Self-Efficacy

M-commerce, or mobile commerce, has gained popularity due to its expanded possibilities over Electronic commerce, which has been growing at a far faster pace because to the Covid-19 outbreak in Pakistan. The utilization of wireless technologies has increased in the M-commerce sector. There are more mobile phone users than personal computer users. In spite of Pakistan's apparent interest in M-commerce as a means of trade, nothing is known about the country's citizens' desire to utilize this new wireless electronic platform. Thus, customers' behavioral intention to use m-commerce has been investigated using an improved technology acceptance model (TAM) that takes into account the consequences of social influence and mobile self-efficacy. The sample size for this study was 220 people from Lahore, Karachi, Islamabad, and Multan, which is a statistically significant number. A variety of statistical techniques, including factor and reliability analysis as well as correlation and regression, were used to examine the data by using SPSS version 19. Customers' attitudes toward adopting M-commerce are positively influenced by their perceptions of its utility, ease of use, social influence, and personal efficacy on their mobile devices, according to this study, which also found a link between these factors and their behavioral intent to use M-commerce.

Cyber Security Challanges for Online Trading

bstract: Today Security is a fundamental component in the computing and networking technology. The first and foremost thing of every network designing, planning, building, and operating a network is the importance of a strong security policy. Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern. The internet structure itself allowed for many security threats to occur. Online trading facilitates as a financial platform form for buying and selling shares, bidding, product sale or purchase using computers. The vital online trading between B2B, B2C and C2C transaction data may be under siege due to spywares, malwares, Hijackers and intruders. Often trading secrets and portfolios are in risk with network attackers who tracks unauthorizedly the personalized data of business clients. Cyber security is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It's also known as information technology security or electronic information security. The term applies in a variety of contexts, from business to mobile computing, and can be divided into a few common categories.

Data management system implemented in wristbands with RFID technology (SAYETS)

In the last decade, the loss of elderly family members has been a major problem, as well as in traffic accidents, therefore, it is important to know personal information such as medical issues for the proper care of those affected. SAYETS, is a project that aims to design an administration and data management system implemented in a wristband with RFID technology, so that the information is transmitted to different users. The project contributes to the health sector, since there are emergency cases in which it is not possible to disclose the necessary information about the health of the affected elderly, such their medical history with personal information (name, age, address) so as emergency contacts (names, telephone numbers, addresses, relationship). SAYETS can also be applied in several areas, such as work and personal usage

Komunikasi Dalam Pengembangan Hubungan Antarpribadi Melalui Second Account Instagram

Human beings have a need to express themselves and define their identity. With the existence of a second account on Instagram, individuals can express aspects that may not be fully revealed on the main account. The study aims to understand communication in the development of interpersonal relationships in users and followers of second Instagram accounts. The method used is qualitative descriptive research with a constructivism research paradigm. The theory used is Social Penetration Theory which explains the development of interpersonal relationships that include depth and breadth in interaction. The results showed that the younger generation, especially students, use Instagram second accounts to form relational partners through relationship development. Relationship development is done by means of self-disclosure, both in terms of depth and breadth and establishing presence over time. Each user's distinct personality determines the level of self-disclosure. Communication moves from the exploratory stage to a more intimate relationship depending on whether the relationship produces positive benefits. If the user feels the benefits, then the information shared will show intimacy (mutual understanding and support), conversely, if the user does not feel the benefit or satisfaction of the relationship with someone who is his follower, then the user will hide the followers from the close friends feature or unfriend in the second account.

Achieving Algorithmic Transparency and Managing Risks of Data Security when Making Decisions without Human Interference: Legal Approaches

Objective: to compare modern approaches in law to the use of program codes and algorithms in decision-making that meet the principles of transparency and openness, as well as the increasingly stringent requirements for ensuring the security of personal and other big data obtained and processed algorithmically.Methods: the main methods for researching the principle of transparency in algorithmic decision-making were formal-legal and comparative analysis of legal acts and international standards of information security, as well as the principles and legal constructions contained in them.Results: it was determined that the development of information security standardization, inclusion in legal acts of requirements for the development of information technologies that comply with the principles of transparency and openness of applied algorithms will minimize the risks associated with the unlawful processing of users' big data and obtaining information about their privacy. Proposals were identified, related to the implementation of algorithmic transparency in the field of data processing legal regulation. Recommendations were formulated, based on which the legislator can solve the problem of ensuring the openness of the logic of information technology algorithms with regard to modern standards of information security.Scientific novelty: it consists in the substantiation of new trends and relevant legal approaches, which allow revealing the logic of data processing by digital and information technologies, based on the characterization of European standards of the “privacy by design” concept in new digital and information technologies of decision-making and data protection, as well as on the new legal requirements for artificial intelligence systems, including the requirement to ensure algorithmic transparency, and criteria for personal data and users' big data processing. This said, data protection is understood as a system of legal, technical and organizational principles aimed at ensuring personal data confidentiality.Practical significance: it is due to the need to study the best Russian and international practices in protecting the privacy of users of digital and information technologies, as well as the need for legislative provision of requirements for the use of algorithms that meet the principles of transparency and openness of personal data processing, taking into account the need to ensure confidentiality at all stages of the life cycle of their processing, which will ensure the continuity of security management.

Forward Chaining Algorithm on Informatics Graduate Job Recommendation System Based on MBTI Test

The Myers-Briggs Type Indicator (MBTI) is a method for identifying an individual's personality type based on the psychological theory of Carl Gustav Jung. In the context of computer science students, they often face challenges in planning their academic journey and determining the direction of their career development during their studies, causing confusion when it comes to choosing a career path in the field of computer science in the future. To address these challenges, the researcher has developed a web-based expert system using the PHP programming language. This expert system is designed to make decisions based on a collection of user responses, which are processed using the forward chaining method, ultimately providing the user's personality type along with suitable career choices. The primary objective of the expert system is to assist students in making decisions regarding their studies and future careers. Through this research, the researcher has produced a functioning website capable of efficiently processing user responses and generating decisions regarding personality types and career options. Thus, this study provides a solution to aid computer science students in planning their academic and career paths.

Character expression of a conversational robot for adapting to user personality

User adaptation is important in conversational robots to enhance the user experience and engagement. One way of user adaptation is to change the dialogue behavior. A character (e.g. extrovert or agreeable) can be defined to achieve human-like behaviors and user adaptation, and the appropriate character differs depending on each user. In this study, we investigate user adaptation by character expression of conversational robots. Our character expression model adopts the Big Five traits for controlling four dialogue behaviors: amount of utterance, backchannel frequency, filler frequency, and switching pause length. We cluster the system character and user personality into four classes based on an analysis of a speed dating dialogue corpus, and we found the best combinations between the system character and user personality. We implemented the character expression model into a laboratory guide and a chit-chat robot and conducted subjective experiments, where the subjects talked with robots with four different characters and evaluated their impressions of each character. The results shows that the Role model character was preferred for the task-oriented dialogue of the laboratory guide, but a robot character complementary to the subject personality was preferred for the non-task-oriented dialogue of the speed dating and chit-chat.

User-Guided Enhancements to a Technology-Facilitated Resilience Program to Address Opioid Risks Following Traumatic Injury in Youth: Qualitative Interview Study.

Youth with traumatic injury experience elevated risk for behavioral health disorders, yet posthospital monitoring of patients' behavioral health is rare. The Telehealth Resilience and Recovery Program (TRRP), a technology-facilitated and stepped access-to-care program initiated in hospitals and designed to be integrated seamlessly into trauma center operations, is a program that can potentially address this treatment gap. However, the TRRP was originally developed to address this gap for mental health recovery but not substance use. Given the high rates of substance and opioid use disorders among youth with traumatic injury, there is a need to monitor substance use and related symptoms alongside other mental health concerns. This study aimed to use an iterative, user-guided approach to inform substance use adaptations to TRRP content and procedures. We conducted individual semistructured interviews with adolescents (aged 12-17 years) and young adults (aged 18-25 years) who were recently discharged from trauma centers (n=20) and health care providers from two level 1 trauma centers (n=15). Interviews inquired about reactions to and recommendations for expanding TRRP content, features, and functionality; factors related to TRRP implementation and acceptability; and current strategies for monitoring patients' postinjury physical and emotional recovery and opioid and substance use. Interview responses were transcribed and analyzed using thematic analysis to guide new TRRP substance use content and procedures. Themes identified in interviews included gaps in care, task automation, user personalization, privacy concerns, and in-person preferences. Based on these results, a multimedia, web-based mobile education app was developed that included 8 discrete interactive education modules and 6 videos on opioid use disorder, and TRRP procedures were adapted to target opioid and other substance use disorder risk. Substance use adaptations included the development of a set of SMS text messaging-delivered questions that monitor both mental health symptoms and substance use and related symptoms (eg, pain and sleep) and the identification of validated mental health and substance use screening tools to monitor patients' behavioral health in the months after discharge. Patients and health care providers found the TRRP and its expansion to address substance use acceptable. This iterative, user-guided approach yielded novel content and procedures that will be evaluated in a future trial.

Investigation the Intervention Mitigating Problematic Social Media Engagement Among High School Students

Adolescents of 21st century mature in an advancing digital era, where their vulnerabilities are often exposed to a world driven by powerful algorithms. The present study proposes an innovative design scheme that potentially tackles problematic engagement with social media, from both motivational and neurobiological perspectives, with the aim of unveiling the underlying mechanisms regarding social media addiction. This design solution promotes transparency of individuals’ personal usage behaviours through a simple prompt, “why are you here?”, thereby activating individual’s deliberate decisionmaking system. In study I, we conducted a two-phase survey involving 92 high school teenagers and collected empirical data on the correlations between goal-oriented social media use and mental health variables. In addition, a practical design intervention was developed and assessed with 36 participants over ten days to validate its effectiveness. The consequences indicated notable reductions in actual social media engagement time, as well as a decrease in distractedness. This research offers a novel motivation-driven digital behaviour and contributes to the existing empirical database on social media addiction by providing both theoretical insights and practical applications.

Social Media Effect on Personal Self-Esteem Among the Population in Saudi Arabia.

Background Self-esteem is a self-valuation; it is how people perceive their own worth and how valuable they believe they are to others.In this study, our primary objective was to explore the association between social media use and self-esteem among individuals who actively engage with social media platforms in Saudi Arabia. Method This study involved individuals aged 15 and above who are active social media users residing in Saudi Arabia. The data were collected through an anonymous online cross-sectional survey. Participants were recruitedusingsnowballandconveniencesampling method. A questionnaire was administered through Google Forms to collect data from participants. The questionnaire was structured into three sections, which included gathering social and demographic information, assessing personal usage patterns, and evaluating individual self-esteem levels using an Arabicvalid version of the Rosenberg Self-Esteem Scale. Results The survey included a total of 2,551 participants. Among them, 51.3% (n = 1,309) were female, 29% (n = 741) fell within the 21-25 age group, 95.7% (n = 2,441) were of Saudi nationality, and 51.6% (n = 1,316) were single. The social media platform most frequently used by participants was TikTok98.5% (n = 2,512), followed by Facebook 95.7% (n = 2,441), Telegram 89.8% (n = 2,291), YouTube 72.2% (n = 1,942), WhatsApp 66.0% (n = 1,683), and finally, Snapchat 30.7% (n = 1,769). In total, 14.3% (n= 366) have low self-esteem, participants scored around 16.10 ± 1.80, ranging from 5 to 25. The following variables were significantly associated with self-esteem: female sex (83.88% vs 87.52%,X2= 6.87,p= 0.009), nationality (X2= 13.507, p < 0.001),marital status (X2= 12.313,p= 0.006), region (X2= 18.36,p= 0.001), using Tik Tok(X2= 4.11,p= 0.043), the frequency of posting comments (X2= 12.01,p= 0.017), comparing oneself to others (X2= 27.94,p< 0.001), using social media because of weak personal communication (X2= 6.56, p = 0.010), using social media to follow news (X2= 6.89,p= 0.009), and the perceived effect of social media (X2= 16.28,p< 0.001). Conclusions Our findings revealed that a minority of participants exhibited low self-esteem, and individuals from the Northern region were more likely to report such issues. Sociodemographic factors, including gender, nationality, and marital status, demonstrated associations with self-esteem. Additionally, the frequency of comments, TikTok usage, and peer comparison significantly influenced self-esteem levels.

An approach for proactive mobile recommendations based on user-defined rules

In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing.In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user’s mobile device, reducing wireless communication and safeguarding the user’s privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users’ needs.

A Privacy Preserving System for Movie Recommendations Using Federated Learning

Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.

Do personality traits influence the user’s behavioral intention to adopt and use Open Government Data (OGD)? An empirical investigation

The academic interest in the Open Government Data (OGD) domain has been burgeoning over the years. Conceding that the prime focus of an OGD initiative is its further re-use for value creation and innovation by stakeholders, the present study seeks to underscore the role of HEXACO personality traits on behavioral intention (BI) to adopt and use OGD in developing countries' context. We investigate the direct, indirect, and moderating effects of HEXACO personality traits provide a better understanding of how and to what extent personality traits influence future behavioral intention to use OGD. The results demonstrate that Trust and Performance Expectancy are positive predictors of BI to adopt and use OGD. Users with higher Openness to Experience tend to have higher Effort and Performance Expectancy; are characterized by exposure to Social Influence; have higher level of Trust and positive experience of Facilitating Conditions and Information Quality. Agreeable people are more likely to Voluntarily Use OGD. Conscientiousness enhances the individual's perception of OGD quality-related factors. Excessive Emotionality affects negative perception to System and Information Quality issues. Honesty–Humility and Extraversion are able to maintain the effect of OGD Information Quality and Trust on users' BI. Our findings could be useful for practitioners to level the divergence between actual and potential use of OGD by considering the user's personality traits..

A Review of User Identification Methods Based on Digital Fingerprint

Methods of user identification based on digital fingerprints are considered. The main approaches for the browser fingerprints creation which is installed on the user's device and characterizes the device belonging to the user are presented. The methods used to identify a person (user) during the operation of the device are also described. Methods using both the dynamics of keystrokes and interactions with the touch screen, voice and geolocation data, as well as behavioral biometrics and behavioral profile are presented. The concept of continuous authentication is described as a development of the identification approach. A list of publicly available data sets mentioned in the studies reviewed in the review is provided, with links to download them.