Active Users Research Articles

Ethical keylogger solution for monitoring user activities in cybersecurity networks

This paper presents the development of a software keylogger to monitor user activities within a cyber network, enhancing security and facilitating comprehensive cybersecurity analysis. Guided by the agile methodology, the work adopts a flexible and iterative approach to development, ensuring continuous collaboration, adaptation to evolving requirements, and swift response to feedback. Leveraging the versatility and efficiency of the Python programming language, this work transforms the traditional notion of a keylogger, which is a program that records keystrokes, into an ethical and legally compliant tool. By capturing and logging keystrokes, the system provides valuable insights into user behavior and potential security threats, enabling proactive measures to prevent cyber-attacks and data breaches. Prioritizing transparency and consent, the system design adheres to ethical standards and privacy regulations, ensuring the protection of sensitive information and user privacy. This technical solution offers cybersecurity professionals, system administrators, and organizations a valuable tool for network monitoring while showcasing responsible development practices and contributing to the advancement of ethical cybersecurity solutions.

Adaptive impact analysis in flexible multibody systems based on hierarchically refined IGA models

AbstractUsually, detailed impact simulation models within flexible multibody systems have to be set up manually rather than being generated automatically. This is because the process requires prior knowledge of the time and location of the impact, as well as the element resolution within the contact area. If the penalty method is used to determine the occurring contact forces, the corresponding penalty factor also needs to be determined manually. This work, however, presents an adaptive algorithm to simulate impacts within flexible multibody systems fully automatically using reduced isogeometric analysis models, the floating frame of reference formulation, and quasistatic contact models for an efficient but still accurate simulation. The adaptive algorithm detects impacts in the system, determines the contact locations on the bodies, refines the contact area, and determines the penalty factor, and therefore automatically simulates impacts. The work shows how to automatically simulate impacts in flexible multibody systems without user action or prior knowledge of impact location and size. The first application example simulates significant elastodynamic effects within a long flexible rod. The goal is to validate the algorithm by preserving the wave propagation and energy of the system. The second application example simulates the impacts of two flexible double pendulums. This setup is a suitable benchmark for the complete adaptive impact analysis procedure as the flexible double pendulums undergo large rigid body motions.

The Development of Modeling Shared Spaces to Support Sustainable Transport Systems: Introduction to the Integrated Pedestrian–Vehicle Model (IPVM)

The significance of developing shared road infrastructure in cities throughout the world is growing. Driven by the need to improve traffic management in ways that enhance multiple sustainability outcomes, developing the tools needed to test shared space proposals is becoming more sought after by responsible agencies. This paper reviews approaches to simulation modeling focused on representing and assessing shared spaces, culminating in a new approach presented here called the Integrated Pedestrian–Vehicle Model (IPVM)—a novel framework that combines social force models, car-following models and other algorithms from the robotics domain to better describe both mobility and activity within a shared space. The IPVM recognizes that while shared spaces are inherently multimodal, past efforts have tended to use pedestrian models as a starting point. Most consider the interaction of pedestrians with other pedestrians and static road infrastructure. Shared space models are generally microscopic models that integrate a social force model with a variety of car-following models to describe the interaction between vehicles and pedestrians. However, there is little research and few practical methodologies that address the long-range conflict avoidance between vehicles and pedestrians. This aspect is crucial for accurately representing the desire lines and pathways of pedestrians and active transport users in complex environments like shared spaces. The IPVM describes and visualizes shared road infrastructure with an absence of separating infrastructure between users and outputs. It generates metrics that can be used in conjunction with the latest evaluation approaches to gauge the sustainability credentials of shared space road proposals. Enhanced modeling of shared space solutions can lead to more effective implementation, which can potentially reduce the presence of cars, increase public and active transport use and lead to a more sustainable transport system.

Co-designing restrictive practice elimination: A systems thinking approach with mental health service users and practitioners in rural/regional Australia.

Elimination of restrictive practices (physical/mechanical restraint and seclusion) from adult acute mental health care services has been demanded internationally for many decades. This study aimed to: (1) Identify priority issues in the elimination of and use of alternative approaches to restrictive practices (seclusion and physical/mechanical restraint) in rural/regional acute adult mental healthcare services, as told by mental healthcare service users and practitioners, (2) identify the community-based, system-level feedback loops that enhance or reduce the use of restrictive practices and viable alternatives and, (3) identify potential action areas to improve system structures to increase regional mental healthcare services' ability to eliminate restrictive practices and use alternative approaches. Group model building (GMB) workshops were held with a small group (n = 9) of mental healthcare practitioners and service users with lived experience of restrictive practice use. This participatory approach enables exploration and visual mapping of local structures causing behaviour patterns of practitioner and service user concern over time - in this case, the barriers, and enablers to alternative approaches to restrictive practices in adult acute mental healthcare services within the Geelong-Barwon region. This is the first study that specifically applies GMB in the discussion of the elimination of restrictive practices in mental health in the non-metropolitan regional/rural context. Participants identified four key priorities in relation to eliminating restrictive practices: (1) self-advocacy, (2) continuity of care, (3) early intervention, and (4) safety for all. The study findings were distilled into a novel preliminary set of mental healthcare practitioner and service user action ideas.

Depression Prediction using Machine Learning Algorithms

Depression affects millions worldwide, emphasizing the need for early detection. Leveraging machine learning, our research introduces a novel deep learning model merging text and social media data for depression prediction. Comparative analysis with state-of-the-art methods demonstrates promising results. As heightened social media use correlates with increased depression rates, our study targets probable depressed Twitter users through machine learning. By analyzing both network behavior and tweets, we develop classifiers utilizing diverse features extracted from user activities, revealing that incorporating more features enhances accuracy and F-measure scores in identifying depressed users. Our data-driven approach offers a predictive tool for early depression detection and other mental illnesses. This paper contributes insights into depression detection using machine learning and proposes innovative strategies for improved diagnosis and treatment

Making sense of methods and approaches to user involvement

There has been a dramatic growth in the ways of involving users in various product, service, space, and systems development activities. Various categorizations, listings, and mappings have been proposed for making sense of this user-involvement landscape. The present paper introduces the main types of currently available clarificatory mappings used in design for, with, and by users, as well as the shortcomings associated with them. It then proceeds to elaborate how such mappings could be better organized in the future, stressing the importance of distinguishing between techniques, methods, methodologies, and approaches. While sensible mappings are difficult if not impossible to ever achieve regarding techniques and methods, wider approaches in how designer–user relations are organized in time and space do appear to be amenable to differentiation. We elaborate a typology of nine approach-level user-involvement configurations based on resemblance relations, and how these in turn help make sense of wider research areas.

Effectiveness of Gerontechnology Empowerment Program on Awareness and Use of Mobile Apps Among Older Adults for Instrumental Activities of Daily Living: Protocol for a Cluster Randomized Controlled Trial.

Instrumental activities of daily living (iADLs) are crucial for older adults to live independently. Health care and technological advancements will increase the older adult population and life expectancy globally. Difficulties with iADLs impact older adults' quality of life. Mobile apps can assist older adults, but many require help due to limited awareness. Lack of awareness is a barrier to app use. Existing literature mainly covers health care and app design, needing more focus on iADL apps for older adults. The study objectives encompass 2 main aspects: first, to evaluate the awareness, use, and factors influencing the use of apps among older adults for iADLs; and second, to create and assess the effectiveness of a gerontechnology empowerment program (GEP) for older adults on the awareness and use of apps for iADLs. This research uses a quantitative approach divided into 2 distinct phases. In phase 1, we conduct a descriptive survey to assess the level of awareness and use of mobile apps for iADLs and identify the factors that influence the use of such apps among older adults. To ensure clarity and comprehension among participants, we provide them with a subject information sheet in both Kannada and English. The data collected during this phase enable us to gain insights into awareness levels, use patterns, and factors that shape older adults' use of apps for iADLs. The results serve as the foundation for designing the GEP. In phase 2, a cluster randomization method will be used to select older adults aged 60 to 75 years in Udupi district, Karnataka, India, who are active smartphone users. These participants will be divided into 2 groups: the experimental and the control groups. The experimental group will join the GEP. The sample size for phase 1 is 554, and phase 2 is 50. To assess the effectiveness of this program, we will measure the outcomes before and after its implementation using the same assessment tools used in phase 1. This study is funded by the Indian Council of Medical Research (Adhoc/193/2022/SBHSR on November 18, 2022). Phase 1 data collection is expected to be completed by November 2023, and phase 2 is scheduled to commence in the upcoming months. Phase 1 and 2 findings will be analyzed and discussed in the main paper, which we intend to submit to a high-quality peer-reviewed journal for publication. The research protocol, informed consent forms, and associated documentation received approval from institutional ethics committees (214/2020). Upon the successful testing of the GEP, it can be recommended that welfare departments encourage older adults to use mobile apps for iADLs and establish training programs to provide support to older adults in using these apps. Clinical Trials Registry - India CTRI/2020/09/027977; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=NDUxMzM=&Enc=&userName=027977. DERR1-10.2196/53587.

ENHANCING DEEPFAKE DETECTION: LEVERAGING MTCNN AND INCEPTION RESNET

In response to the escalating threat posed by manipulated facial imagery in the digital age, our research project is dedicated to developing an advanced framework for the detection and classification of such content, augmented by a proactive user reporting mechanism. Leveraging state-of-the-art deep learning models like Multi-Task Cascaded Convolutional Networks (MTCNN) and InceptionResnetV1, our framework achieves an impressive accuracy rate of 92% in distinguishing between genuine and manipulated faces. The integration of explainability methods such as Grad-CAM enhances model interpretability, empowering users to understand model predictions. Additionally, our user-centric reporting interface enables active user participation in identifying and flagging potentially manipulated content, fostering transparency and accountability in digital media platforms. With the continued proliferation of deepfake technology, our research endeavors not only advance facial image analysis techniques but also uphold principles of trust and integrity in the digital realm, aiming to safeguard the credibility of information dissemination through vigilance, innovation, and collaborative action. Keywords: Deepfake detection, Facial image manipulation, Deep learning models, User reporting mechanism, Model interpretability, Transparency in digital media, Trust and integrity, Information dissemination, Vigilance, Collaborative action.

Wavoice: An mmWave-Assisted Noise-Resistant Speech Recognition System

As automatic speech recognition evolves, deployment of the voice user interface (VUI) has boomingly expanded. Especially since the COVID-19 pandemic, the VUI has gained more attention in online communication owing to its non-contact property. However, the VUI struggles to be applied in public scenes due to the degradation of received audio signals caused by various ambient noises. In this article, we propose Wavoice , the first noise-resistant multi-modal speech recognition system that fuses two distinct voices sensing modalities (i.e., millimeter-wave signals and audio signals from a microphone) together. One key contribution is to model the inherent correlation between millimeter-wave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Additionally, we elaborate on two novel modules for multi-modal fusion embedded into the neural network, leading to accurate speech recognition. Extensive experiments prove the effectiveness of Wavoice under adverse conditions—that is, the character recognition error rate below 1% in a range of 7 m. In terms of robustness and accuracy, Wavoice considerably outperforms existing audio-only speech recognition methods with lower character error and word error rates.

Does social currency influence social media usage and brand experience: an empirical analysis

PurposeLoading the work on social capital theory, we have tried to check the influence of social currency (SC) on social media usage (SMU) and its subsequent impact on the brand experience (BE). The study attempted to ascertain the mediation of loyalty (LT) between the SC and SMU as well as the moderation of perceived ease of use (PEU) in the SMU and BE relationship.Design/methodology/approachThe data were collected from 384 respondents employing snowball sampling technique by administering a questionnaire on seven-point Likert scale. The respondents were the active internet users, who devote the significant part of their daily routine to social media activities. Structural equation modelling was used to study the path of the considered variables.FindingsThe result of the study strengthened the findings of earlier studies on SC dimensions. This study elaborated about the positive impact of SC on SMU, SC on LT, LT on SMU, SMU on BE. The study also found the mediating effect of LT in the SC and BE relationship. The study did not identify a moderating role of PEU in the SMU and BE relationship.Research limitations/implicationsBy taking into account SC, SMU, LT, PEU and BE, the study adds the knowledge to the social capital theory.Practical implicationsThis study aids marketers in adjusting social media strategies for the effective use of SC aimed to deliver enhanced BE. Additionally, it demonstrates the value of customer LT in the context of SC and SMU by customers.Originality/valueThis study is among the few research on the SC of the Indian user and its impact on SMU and BE. Checking the mediation of LT and the moderation of PEU in the SC context makes this study novel and different from any study conducted earlier.

Sentiment Analysis on Twitter through Machine Learning: A Comprehensive Approach with User-Centric Visualisations

Sentiment analysis is a crucial field that deals with the intricate task of identifying and systematically categorizing the various perspectives and opinions expressed within the original text. In today's digital age, social media platforms serve as a prolific source of data, inundated with a relentless stream of status updates, tweets, and content imbued with sentiments. Analysing the sentiments conveyed by users in this vast reservoir of data holds a pivotal role in comprehending the collective sentiments of the user community, dissecting dialogues, and aggregating viewpoints. This, in turn, can be instrumental in shaping strategies for commerce, conducting insightful political research, and gauging the pulse of communal activities. Examining sentiments on Twitter presents an increased difficulty because of the frequency of spelling errors, casual language, icons, and emojis. This research focuses on Twitter sentiment analysis, with a specific emphasis on a particular user account. The approach involves a combination of Python programming and Machine Learning techniques. By embarking on a comprehensive sentiment analysis journey within a specific domain, the aim is to discern the profound impact of that domain's data on sentiment categorization. Furthermore, this paper introduces a novel feature that enhances the organization of a user's most recent tweets and their presentation through visual aids such as graphs, charts, and word clouds. This visualization approach empowers a more intuitive and insightful exploration of the sentiments and trends embedded within the user's Twitter activity, facilitating a deeper understanding of their thoughts and emotions as expressed through their digital interactions.

Navigating influence: Unraveling the impact of micro-influencer attributes on consumer choices in the Chinese social media

This study aims to explore the relationship between consumer purchasing behavior and key micro-influencer attributes, including knowledge, entertainment value, credibility, and transparency, within the context of Chinese social media platforms. The paper adopts a quantitative approach, employing partial least squares structural equation modeling (PLS-SEM) to analyze the intricate relationships among latent variables. The respondents comprise active users of major Chinese social media platforms, such as Weibo and Xiaohongshu. For primary data collection, 329 respondents were surveyed online, utilizing a convenient sampling method as part of non-probability sampling. Data collection spanned four weeks, and participants were given the option to respond in either English or Mandarin. The findings suggest significant associations between consumer purchasing behavior and micro-influencer attributes. Specifically, knowledge, entertainment value, credibility, and transparency exhibit varying degrees of influence on consumer behavior within the Chinese social media landscape. The p-value for H1, H2, H3, and H7 appeared as 0.000 and shows that these are the highly significant relations, whereas the p-value for H3 (0.019), for H5 (0.001), and for H6 (0.028) shows that these relations play a moderate role in the proposed model. Elucidating the role of key attributes provides valuable insights for marketers and businesses seeking to leverage micro-influencer marketing strategies effectively in this rapidly evolving digital landscape.

Evaluating the effects of hormone therapy termination on skeletal muscle and physical independence in postmenopausal women.

In women, the age-related decline in skeletal muscle structure and function is accelerated after menopause, which implicates the role of decreased circulating estrogen levels. Indeed, boosting estrogen, by means of postmenopausal hormone therapy (HT), generally proves beneficial to skeletal muscle. The evidence regarding whether these benefits persist even after cessation of HT is limited, nor is it clear how physical behavior (PB) impacts on benefits. Hence, this exploratory study focused on the interplay between HT administration/cessation, PB and in vivo skeletal muscle structure and function. Fifty healthy women (≥60 y) were included; 19 had an HT administration history (≥9 mo, with now ~8-y hiatus in treatment) and 31 no such history. On seven continuous days, PB data were collected using triaxial accelerometry and analyzed using compositional data analysis. Gastrocnemius medialis muscle volume, architecture, and function were determined using ultrasonography, electromyography, dual x-ray absorptiometry, and dynamometry. Current serum estradiol levels were measured using ELISA. Only fascicle length and duration of HT administration were positively associated. With respect to PB levels, we found a pattern suggesting greater vitality (higher physical activity and lower sedentarism) in previous HT users, compared with nonusers, despite the two groups currently no longer exhibiting significantly different levels of circulating estradiol. After an 8-year hiatus in treatment, HT provides limited advantages in gastrocnemius medialis muscle properties. Interestingly, it perhaps enhances vitality despite prolonged cessation, which in the longer term would facilitate greater physical independence, especially considering the association of sedentary behavior with greater frailty.

Video apps user engagement and stock market volatility: Evidence from China

This paper explores the relationship between the user activity of major comprehensive video apps in China and stock market volatility. The online user counts of mainstream video apps can significantly forecast the volatility of the Chinese stock market. Furthermore, integrating these factors demonstrates excellent predictive performance in different horizons. The reliability and consistency of the empirical results are further confirmed by using MCS and DoC tests.

The consequences of generative AI for online knowledge communities

Generative artificial intelligence technologies, especially large language models (LLMs) like ChatGPT, are revolutionizing information acquisition and content production across a variety of domains. These technologies have a significant potential to impact participation and content production in online knowledge communities. We provide initial evidence of this, analyzing data from Stack Overflow and Reddit developer communities between October 2021 and March 2023, documenting ChatGPT’s influence on user activity in the former. We observe significant declines in both website visits and question volumes at Stack Overflow, particularly around topics where ChatGPT excels. By contrast, activity in Reddit communities shows no evidence of decline, suggesting the importance of social fabric as a buffer against the community-degrading effects of LLMs. Finally, the decline in participation on Stack Overflow is found to be concentrated among newer users, indicating that more junior, less socially embedded users are particularly likely to exit.

Mengukur Pengaruh Kualitas Pelayanan Terhadap Kepuasan dan Loyalitas Pengguna Perpustakaan UMS

This research aims to evaluate the impact or effects of effect of Service, information control, and the Library as a Place on the satisfaction and loyalty of library users. The samples in this test were active students and users who utilized library services at the Muhammadiyah University of Surakarta. Sampling was done using a purposive sampling technique to obtain a total of 150 respondents. The data management is carried out using the SmartPLS 3.0 statistical calculation tool and it can be stated that the effect of Service, Information Control, and Library as Place has a significant effect and has a positive impact on user satisfaction and loyalty so the hypothesis can be accepted.

Vaccine effectiveness against emerging COVID-19 variants using digital health data

BackgroundParticipatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook’s active user base to provide self-reported symptom and vaccination data in near real-time.MethodsHere, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results.ResultsWe highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: −0.40, IQR[−0.45, −0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health’s (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period.ConclusionsParticipatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.

FASTOCH: Feasibility of Electronic Patient-Reported Outcomes in Older Patients With Cancer-A Multicenter Prospective Study.

Multiple studies have demonstrated that electronic patient-reported outcomes (ePROs) improve overall survival and quality of life in cancer care. However, there are no specific prospective data on remote ePRO monitoring in the older population, although they represent a significant proportion of patients with cancer. From February 2021 to April 2022, patients age 75 years and older under active anticancer treatment were consecutively recruited in six institutions. Remote ePRO feasibility was determined in intention-to-test (ITT) on the basis of the number of active users in the overall population. Primary failure applied to patients who had no Internet access or declined to test ePROs, while the other patients were assigned to the ITT population. Feasibility was also determined per-protocol on the basis of the number of active patients in the ITT population. Of the 473 patients included, primary failure applied to 288 patients (233 of whom had no Internet access). Among the 185 patients in ITT, 122 used ePROs, leading to a 26% feasibility in ITT and a 66% feasibility per protocol. In a multivariate analysis, the intent to test population was from a higher socioprofessional category (P = .009) and felt in better general condition in the Geriatric 8-score evaluation (P = .002). Active patients significantly differed from the inactive on their self-assessment of a better general condition (P < .001) only. Our multicenter study showed a limited feasibility rate (26%) of remote ePROs monitoring for older patients with cancer, mainly because of technology barriers. Yet, among the patients who did have Internet access, most of them indeed used ePROs (66%). Given the expected benefit of ePROs, the technology barriers therefore need to be lifted to improve cancer care in older patients.

Evaluating Academic Information Systems Through Dual Models

Evaluation of a system is needed to determine whether the system is still following organizational goals. For maximum results, evaluation should be done regularly. An academic information system is a system that aims to perform data processing so that it can provide convenient information that is used by users in academic administrative activities. This study proposes to determine the level of governance maturity in the monitoring and Evaluation domain of academic information systems at University ‘X’ a private in Indonesia and determine factors of the success of the information system. The results of the measurement process show that the maturity level of the system is at level 2.1 and the factors that affect the success of the system are System Quality, Quality of Service, User Satisfaction, and Net Benefits. The comparison specifically on System Quality and User Satisfaction relation has a less significant impact (0.2) while the IT Performance of the Monitor and Evaluate process is at the initial/ad hoc stage (level 1). In this study, the Quality of Information does not affect the success of the system. The results of this study provide recommendations for organizations to improve governance according to organizational goals to level 3 and to improve the Quality of Information in information systems.

IMPACT OF SOCIAL MEDIA INFLUENCERS ON CONSUMER PURCHASE DECISION

The data provides useful insights into social media users' preferences and actions surrounding influencer marketing across various platforms and demographics. Instagram is the most popular platform, with Facebook maintaining a balanced user base and YouTube attracting more male users. Females choose fashion and beauty products, whereas guys prefer technology and electronics. Younger generations are increasingly active in influencer marketing, demonstrating their importance in this space. Trust in influencer recommendations varies with age, with younger populations showing higher levels of trust. The majority of respondents base their purchases on influencer recommendations, revealing influencers' powerful influence on consumer behavior. Furthermore, the domination of product suggestions based on influencer endorsements reveals social media influencers' immense impact, particularly among younger viewers.. However, there are challenges to authenticity and transparency in influencer marketing. Instagram, in particular, is being questioned about its authenticity, leading users to lose faith. Deceptive endorsements on this media typically lead to unfollowing, highlighting the importance of authenticity in maintaining audience engagement and confidence. Furthermore, there is skepticism regarding influencer transparency when it comes to releasing agreements, indicating that influencer-brand relationships should be more honest and clear. Overall, these insights highlight the changing nature of influencer marketing and the necessity for businesses and influencers to embrace authenticity and transparency in order to develop meaningful connections with their audience in the digital sphere.