Active Users Research Articles

Development of a Cloud Computing-Based Collaboration Information System

The rapid advancement of information and communication technology has created a demand for efficient and effective information systems to facilitate collaboration among institutions. This study develops a cloud computing-based collaboration information system designed to enhance real-time communication, data management, and collaboration among users. The system comprises key modules, including collaboration management, document management, and integrated collaboration features. Testing involved various scenarios with 10 to 100 active users simultaneously, revealing that the system maintained stable performance with response times under 3 seconds for each user request. Furthermore, security assessments utilizing encryption protocols and two-factor authentication confirmed that the managed data is protected from external threats. The findings underscore the system's potential to improve collaborative efficiency while ensuring data security, making it a valuable solution for future collaborative initiatives.

Can AI Be Your Teammate or Friend? Frequent AI Users Are More Likely to Grant Humanlike Roles to AI

The purpose of this research was to identify the comfort levels of professionals with AI in various humanlike roles. A survey of 787 full-time working adults showed that more active AI users are comfortable with AI in many humanlike roles, such as a teammate or a performance coach. Less active AI users, however, are uncomfortable with AI in these roles. Leaders, managers, and educators should prepare employees and students to responsibly address the social and psychological outcomes of increasingly humanlike AI.

The impact of AI-generated content on content consumption habits of Chinese social media users through Xiaohongshu application

This study aims to analyze the effects of artificial intelligence-generated content and human-generated content on user cognition, interaction and satisfaction in Xiaohongshu application. The samples are 500 active Xiaohongshu users in China's first - and second-tier cities, including white-collar workers, students, freelancers selected through multi-stage random sampling. The research instrument is a questionnaire. Data were analyzed by statistical methods such as multiple linear regression analysis, T-test and Pearson correlation analysis were used to compare the differences between AI-generated content and human-generated content in terms of user satisfaction, time of use, recommendation quality, personalization satisfaction, content creativity and understanding of user needs. The findings revealed that AI-generated content shows significant advantages in improving user satisfaction and interaction, and the conclusions provide empirical support for the content optimization of social media platforms.

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. The results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

Is cannabis a slippery slope? Associations between psychological dysfunctioning, other substance use, and impaired driving, in a sample of active cannabis users.

Cannabis is a gateway drug that can lead to the engagement of other substances. Psychological dysfunctioning and dependence have been highlighted as primary components to substance misuse. The purpose of this study was to investigate what aspects of cannabis use and psychological dysfunctioning are associated with the engagement of other substances and impaired driving. Subject to screening, 200 active adult cannabis users completed an online survey. Existing data involving non-cannabis users (N = 833) were also implemented as comparative data. The comparisons suggested that cannabis users were far more likely to have used other drugs in the past 12 months, compared to non-cannabis users. Bivariate correlations and multiple regressions indicated that the degree of cannabis use and likely dependence, psycho-social motives for using cannabis, emotion dysregulation, and psychopathology were positively associated with the frequency of using and driving on other substances. Finally, an ANOVA demonstrated that outside of age, there were no apparent differences in substance use behaviours, motives for using cannabis, and psychological dysfunction, between medicinal and black-market cannabis users. These findings highlight the potential benefits of incorporating self-regulatory concepts into current road safety initiatives, which aim to reduce the interconnected issue of substance misuse and impaired driving behaviours.

Temporalities of e-scooter practices and their systemic effects: The case of the city of Poznań, Poland

ABSTRACT Studies of e-scooters’ usage mushroomed in the social sciences almost as quickly as the new means of urban mobility themselves appeared in many European cities. After almost five years of their presence in Poland, it is still not clear whether e-scooters will and should remain part of the sustainability transition, and how they have contributed to it thus far. The article explores the potential of the social practice approach (SPA) to examine temporalities of e-scooters’ usage at the level of individual practices, their structuration effects, and at the systemic level. It is based on ethnographic interviews carried out with the most active e-scooter users in Poznań, a medium-size city in Poland. The authors find that the temporal characteristics of the e-scooter practices and of their structuring effects suggest that e-scooters induced systemic changes quickly but that their extent and timespan may be limited in many ways. They conclude that even if e-scooters disappear from the spaces in the city of Poznań, micromobility will continue while other means of transport are in use.

Travel mode choice behavior analysis using multinomial logit models towards creating sustainable college campus: a case study of the University of Toledo, Ohio

By employing descriptive statistics and multinomial logistic regression models, the paper investigates factors that affect the travel mode choice behavior of a college community for both commuting to the campus from their residences and commuting on campus from one facility to another. Results show that car ownership, travel distance, age, gender, academic status, income, perception of safety, number of weekly trips, and weather conditions affect people’s mode choice decisions significantly. The study finds that increasing travel distance, age, and being a staff member positively impact car mode choice, faculty members are more likely to bike and walk compared to undergraduate students, and unlike the authors’ predictions, the higher-income people are more likely to walk on campus. Results also reveal that car users change travel modes more often than active transport mode users based on semesters and that although women are less likely to bike to the campus, they are more likely to walk and bike on campus. If the number of trips increases, people prefer cars to walk, bike, and ride a bus to commute to the campus while they prefer walking to driving a car on campus. The study contributes to help create pollution-free and healthy urban college campuses that, on larger scales, will further contribute to developing sustainable cities to achieve the Sustainable Development Goals as described by the United Nations, particularly goal number 11, “to make cities and human settlements inclusive, safe, resilient and sustainable,” and thereby contribute to improving urban living conditions.

A Tale of 102,726 Messages: Characterizing the Modern Urologist's Portal Message Burden After Common Urologic Surgeries.

We aimed to characterize patient portal messaging use after urologic surgery to identify administrative burden and evaluate postoperative clinical associations. Epic was queried for all urologic surgeries performed at the Mayo Clinic enterprise between 2019 and 2022. Data from the highest volume procedures were extracted including patient-generated portal messages to their provider and emergency department (ED) visits within 6 months of surgery. Factors associated with portal users and message volume, as well as the impact of portal use on risk of subsequent ED visit, were evaluated. We analyzed data from 23,621 urologic procedures, which generated 102,726 patient portal messages within 6 months of surgery. We found that 55% of our cohort sent at least 1 message. Stratifying by subspecialty, endourologic surgeries generated the fewest number of messages per surgery (3.83; SD, 8.76), whereas female pelvic medicine and reconstructive surgeries yielded the most (6.05; SD, 10.92). Younger age, female sex, and White race were associated with increased portal utilization. Multivariable time-to-event analysis revealed a 33% reduction in the risk of ED presentation within 90 days after surgery for patients using the patient portal compared with those who did not. While only half of patients sent portal messages after surgery, active users showed a 33% reduction in ED visits, suggesting its potential to reduce health care utilization. Encouraging broader portal adoption can improve outcomes. However, the message burden for urologists necessitates solutions. Resource allocation should prioritize strategies to help urologists manage messages while preserving the established clinical benefits.

WiCAM2.0: Imperceptible and Targeted Attack on Deep Learning based WiFi Sensing

With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential vulnerabilities in deep models has been limited, with existing studies primarily focusing on evaluating wireless adversarial performance in communication or sensing alone. Moreover, there is a lack of a comprehensive definition for attack imperceptibility. In this paper, we come up with a definition of the wireless attack imperceptibility for both communication and sensing. Our objective is to create an adversarial perturbation capable of degrading WiFi sensing performance while preserving WiFi communication integrity. To achieve this, we propose WiCAM2.0 to reveal the temporal and spatial attention of a DNN, capturing the crucial portions of its input. Then we design a mask to confine adversarial perturbations in the attended parts only, minimizing the impact on WiFi communication. WiCAM2.0 is a general adversarial framework that integrates adversarial methods like FGSM and PGD to generate perturbations, capable of initiating both non-targeted and targeted attacks. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets.

Shopee Marketplace Promotion on TikTok: AIDA Model Study on the Impact of Purchasing Behavior of Young Women in Makassar City, Indonesia

This study analyzes the impact of young women’s purchasing behavior on user activity driven by Shopee marketplace promotions on TikTok. This study adopts the Attention, Interest, Desire, and Action (AIDA) model to evaluate purchasing behavior. A quantitative, cross-sectional survey method was employed, involving 283 female adolescent respondents from Makassar City. This study utilizes the AIDA model to assess the independent variables, namely frequency, duration, and consistency, and their influence on the dependent variable, the purchasing behavior process. The findings reveal that purchasing behavior, as measured through the AIDA model, has a positive and significant effect on Shopee marketplace promotions on TikTok. The result of the hypothesis test confirms that Shopee marketplace promotions on TikTok social media influence the product purchasing behavior process. This study further explores various Shopee promotion strategies on TikTok, including discounts, flash sales, cashback offers, free shipping, and product codes. This study offers a new perspective on investigating popular e-commerce platforms in Southeast Asia, particularly among young digital-native consumers. The practical implications suggest that online marketplaces can utilize social media promotions to drive purchases through diverse and engaging promotional strategies.

SIRS (Student Involvement Report System) Case Study – Studies on QOS (Quality of Service) Perimeters, Daily Error Types and Activities Within the Server

Relational Database Management System (RDBMS) is commonly applied in Small, Medium, and Enterprise businesses. In this research, we investigated Quality of Service (QoS) perimeters over day-to-day operations in RDBMS and how the running process able to be expedited with the adaptation and combination of autonomic computing approach using MAPE-K (Monitor, Analyse, Plan, Execute, and Knowledge Base) and Fuzzy rules. The core function of the system is to monitor student attendance, assessment, and lastly escalation of identified student issues to the department of academic. In a nutshell, it is called the Student Involvement Report (Sir). Two segments have been analysed and focused on errors in request time out, bandwidth, hits, and user activities. The outcomes of this are significant on activities that related to the peak of academic activity, due to the submission of student reports and escalation among departments. The MAPE-K and Fuzzy rules can capture the scenarios and identify rules that are significantly important to reduce and enhance the Request Time Out and Hits activities.

Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition.

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound.

Statistical Predictive Hybrid Choice Modeling: Exploring Embedded Neural Architecture

This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications.

Enhancing User Experience on Social Media Platforms: A Flow Theory-Based Approach

In recent years, social media platforms, such as Facebook and Twitter have seen rapid growth and are favored by users of all ages. However, in the age of information overload, the quality of interactions on social media platforms varies significantly, leading to negative feelings among users and affecting their overall experience and loyalty. This study takes the Flow theory as its starting point and investigates the needs of social media users at various stages of the flow experience. We propose modifications to the flow theory model with the aim of enhancing user experience and satisfaction. Firstly, based on literature review, we identify micro-level issues of the product, propose hypotheses, and construct validation experiments in the context of social media. The experiment collects Electrodermal Activity (EDA) and heart rate variability (HRV) data of 31 participants using a multi-channel physiological instrument and collects their emotional experience data using the Pleasure-Arousal-Dominance (PAD) scale. Results indicate that a combined textual-graphical interface layout is superior to other layouts in positively inducing flow and emotional experiences. Positive content tonality is more effective at eliciting positive flow and emotional experiences than negative or neutral content. Video presentations have a stronger sense of immersion and emotional expression, making them more conducive to inducing flow experiences and positive emotional feedback. This study, grounded in user psychology, provides a theoretical framework based on flow and emotional experiences for social media product design, quantitatively examining the positive psychological activities of users when using social media.

Implementing Agile Scrum Methodology in the Development of SMART PEKAN at Batu City Environment Service

The preparation of reports on the activities of the functional position group for the Batu City Environmental Service is still being carried out in the usual way. Making announcements on activities at any time must begin by creating a new page. That matter could be more effective and efficient because the structure of each activity report is the same. This research aims to develop a report creation system whose workings make it easier for users to create activity reports. The system makes the activity reports according to users needs based on the information obtained during interviews and observation. The system is developed using the Agile framework with the Scrum method. This research produces SMART PEKAN (Functional Position Group Activity Report Creation System), a website that makes reports on Functional Position Group activities using automatic report templates. SMART PEKAN can be a tool that facilitates making reports on the activities of users by utilizing the progress of an existing technology to implement digital transformation in the government sector to support the Smart City movement. Keywords: activity reports, agile, innovation, reporting system, scrum, service for the environment

Differences in misinformation sharing can lead to politically asymmetric sanctions.

In response to intense pressure, technology companies have enacted policies to combat misinformation1-4. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias5-7. We argue that differential sharing of misinformation by people identifying with different political groups8-15 could lead to political asymmetries in enforcement, even by unbiased policies. We first analysed 9,000 politically active Twitter users during the US 2020 presidential election. Although users estimated to be pro-Trump/conservative were indeed substantially more likely to be suspended than those estimated to be pro-Biden/liberal, users who were pro-Trump/conservative also shared far more links to various sets of low-quality news sites-even when news quality was determined by politically balanced groups of laypeople, or groups of only Republican laypeople-and had higher estimated likelihoods of being bots. We find similar associations between stated or inferred conservatism and low-quality news sharing (on the basis of both expert and politically balanced layperson ratings) in 7 other datasets of sharing from Twitter, Facebook and survey experiments, spanning 2016 to 2023 and including data from 16 different countries. Thus, even under politically neutral anti-misinformation policies, political asymmetries in enforcement should be expected. Political imbalance in enforcement need not imply bias on the part of social media companies implementing anti-misinformation policies.

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

Mount SMB.pcap: Reconstructing file systems and file operations from network traffic

File system and network forensics are fundamental in forensic investigations, but are often treated as distinct disciplines. This work seeks to unify these fields by introducing a novel framework capable of mounting network captures, enabling investigators to seamlessly browse data using conventional tools. Although our implementation supports various protocols such as HTTP, TLS, and FTP, this work will particularly focus on the complexities of the Server Message Block (SMB) protocol, which is fundamental for shared file system access, especially within local networks.For this, we present a detailed methodology to extract essential file system data from SMB network traffic, aiming to reconstruct the share's file system as accurately as the original. Our approach goes beyond traditional tools like Wireshark, which typically only extract individual files from SMB transmissions. Instead, we reconstruct the entire file system hierarchy, retrieve all associated metadata, and handle multiple versions of files captured within the same network traffic. In addition, we also investigate how file operations impact SMB commands and show how these can be used to accurately recreate user activities on an SMB share based solely on network traffic. Although both methodologies and implementations can be applied independently, their combination provides investigators with a comprehensive view of the reconstructed file system along with the corresponding user activities extracted from network traffic.

Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks

Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals, the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time. We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.

Prospective Study on the Effects of Marijuana Smoking on Lipase and Amylase Activities among Adult Nigerians

Marijuana consumption is prevalent among young adults in Nigeria, and its effects on various physiological systems are of growing concern. This study investigates the impact of marijuana use on digestive enzymes, specifically serum amylase and lipase, to understand its potential implications on digestive health. This cross-sectional study was conducted in 120 participants comprising of 60 marijuana smokers and 60 non-smokers. Sociodemographic and lifestyle data were collected through structured questionnaires. Serum amylase and lipase activity levels were measured by spectrophotometric method and compared between the two groups. Statistical analysis was performed to assess differences and correlations. The study revealed that marijuana smokers had significantly higher serum amylase and lipase activities compared to non-smokers. Lipase activity correlated positively (r=0.425, p=0.019) with duration of marijuana use. Amylase activity was higher among males than females (r=-0.40, p=0.028). No significant correlation was found between the quantity of marijuana consumed and enzyme activities. Chronic marijuana consumption was associated with increased serum amylase and lipase activities, indicating potential alterations in pancreatic function. These findings suggest that marijuana use may have adverse effects on digestive health, warranting further investigation into the long-term implications. It is recommended that healthcare providers monitor digestive enzyme activities in marijuana users to detect early signs of pancreatic dysfunction.