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ObjectivesDespite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.Materials and methodsWe developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.ResultsWe applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.DiscussionWe developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.ConclusionGoing forward, the BDC framework can be used to study engagement with similar dashboards.

A key obstacle in personalised fashion recommendations is the challenge of capturing user physical attributes at a large scale, which limits exclusively computational methods (like machine learning) to readily available attributes whose influence on recommendation accuracy is variable. Expert advice is a potential means of identifying influential user attributes. However, individual experts often disagree or offer conflicting advice. Thus, identifying areas where expert advice is or isn't consistent, in the context of user attributes and profiling is critical. Here, we characterise the breadth of expert definitions of user attributes and profiles through an exhaustive assessment of 156 years of advice literature. Expert definitions of body colouring, shape, and personality attributes are extracted and compared. The range of attribute-value relationships and profile definitions in each domain is described, and coherence among authors for each domain is discussed. [ABSTRACT FROM AUTHOR]

Green roofs are an important part of the ecological system in high-density cities and a crucial component of community public green spaces. Analyzing preferences of residents during spatial planning can help improve their satisfaction and efficiency in using community rooftop spaces. This study uses K-modes algorithm to perform cluster analysis on questionnaires from 699 residents, and summarizes user profiles with significant characteristics by combing the comprehensive ratings of residents regarding different roof space functions. The study shows that differences in preferences among people are not only related to demographic characteristics but also to their interests and environmental perceptions. For example, low-to-middle-income groups, being price-sensitive, tend to reduce energy consumption expenditure. The highly educated population, driven by social needs, shows a clear preference for activity spaces and sports facilities. The elderly population, emphasizing healthy eating and rural memories, gives higher ratings to agricultural spaces. Finally, this study explores the correlation between certain resident characteristics and their preferences for green roof space functions, and proposes a “1+X” spatial configuration strategy dominated by Landscape Leisure Space, with Ecological Low-carbon Space, Agricultural Production Space, and Activity Gathering Space as options, in order to optimize community green roof spaces guided by resident needs.

Two important challenges in recommender systems are modeling users with heterogeneous taste and providing explainable recommendations. In order to improve our understanding of the users in light of these challenges, we developed the attentive multi-persona collaborative filtering (AMP-CF) model. AMP-CF breaks down the user representation into several latent "personas" (profiles) that identify and discern a user's tastes and inclinations. Then, the exposed personas are used to generate, explain, and diversify the recommendation list. As such, AMP-CF offers a unified solution for both aforementioned challenges. We demonstrate AMP-CF on four collaborative filtering datasets from the domains of movies, music, and video games. We show that AMP-CF is competitive with state-of-the-art models in terms of accuracy while providing additional insights for explanations and diversification. [ABSTRACT FROM AUTHOR]

Online romance fraud is a crime carried out largely using language, as the victim and scammer typically do not meet in person in their entire interaction. As a language-enabled crime, a linguistic analysis of scam communication can shed light on how language is used to attract victims and influence their thoughts and actions. This study examined the first stage in the online scam strategy, that is, the putting up of a dating profile (user biography) on online dating service websites. The analysis employs the judgement evaluation framework of appraisal theory to examine the extent to which scammer profiles differ from a set of general user profiles in terms of their use of social approval as a linguistic strategy to attract a more compliant victim type. Findings from the study can help in raising public awareness about how linguistic resources are employed in luring potential victims in scammer dating profiles.

Since pedestrians are impacted by solar radiation differently, urban designers must evaluate solar radiation exposure of pedestrian paths adopting an inclusive approach. This paper proposes a maximum threshold of direct solar radiation exposure for pedestrians based on activity, user profile and environmental conditions, defined as the difference between the energy consumption before feeling exhausted and the energy cost of walking. Two users of diverse walking abilities, a young adult and an elderly person with mobility impairment, were characterised by metabolic activity, walking speed and maximum energy capacity. Based on the theoretical framework, the energy budget of young adults to cope with thermal stress was set as three times higher than for the elderly. This framework was used to quantify the contribution of direct solar radiation to energy balance and then classify walkability during clear-sky summer hours; the term 'walkable' referred to environmental conditions allowing users to walk without feeling exhausted. The methodology was tested on an open area and an urban canyon in Milan; applicability by urban designers was key in developing a simplified way to evaluate shading needs. This approach could be applied to evaluate solar radiation exposure of pedestrian paths adopting diverse user experiences as an evaluation criterion. [ABSTRACT FROM AUTHOR]

As information systems (IS) have evolved, more sophisticated meshing of their positive and negative implications has emerged, leaving users with an increasingly ambivalent experience. Given the discomfort of ambivalence, users intrinsically engage in coping responses with different degrees of cognitive flexibility. This paper investigates ambivalence and the coping responses users adopt in the context of post-adoptive IS use via two research questions: (1) what are the consequences of flexible and inflexible coping responses to ambivalence toward post-adoptive IS use? and (2) how does personality influence a user's disposition toward flexible and inflexible coping responses to ambivalence toward post-adoptive IS use? To address them, we propose and test a research model using a multimethod design, comprising two complementary empirical studies. Study 1 adopts a variable-centered approach to test the hypotheses and to empirically validate the proposed research model. Building on the findings of Study 1, Study 2 employs a person-centered approach to identify a typology of IS users. The findings demonstrate the prevalence of ambivalence among IS users, the dual-nature of their coping responses to ambivalence, in part, influenced by their level of neuroticism, and the associated post-adoptive IS use behaviors. This paper provides a novel perspective to users' attitudes toward an IS use and resolves some of the tensions in prior ambivalence research. [ABSTRACT FROM AUTHOR]

User profiles representing users' preferences and interests play an important role in many applications of personalized recommendation. With the rapid growth of social platforms, there is a critical need for efficient solutions to learn user profiles from the information they shared on social platforms so as to improve the quality of recommendation services. The problem of user profile learning is significantly challenging due to difficulty in handling data from multiple sources, in different formats and often associated with uncertainty. In this paper, we introduce an integrated approach that combines advanced Machine Learning techniques with evidential reasoning based on Dempster-Shafer theory of evidence for user profiling and recommendation. The developed methods for user profile learning and multi-criteria collaborative filtering are demonstrated with experimental results and analysis that show the effectiveness and practicality of the integrated approach. A proposal for extending multi-criteria recommendation systems by incorporating user profiles learned from different sources of data into the recommendation process so as to provide better recommendation capabilities is also highlighted. [ABSTRACT FROM AUTHOR]

Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi‐criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi‐criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi‐criteria rating‐based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power‐item attack model, and introduce its four distinct variants adapted for multi‐criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model‐based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles. [ABSTRACT FROM AUTHOR]

• Factors impacting user engagement in an actual traveler reward app are analyzed. • High initial enrollment among young, female, Asian, and highly-educated residents. • Low-income residents in dense urban areas sustain high engagement. • Denser transportation facilities near homes/jobs enhance program attractiveness. • Nonlinear relations are observed between user engagement and socio-spatial factors. Incentive-based travel demand management (IBTDM) has proven effective in mitigating traffic congestion. However, a comprehensive understanding of factors influencing user engagement in IBTDM is lacking due to limited empirical evidence from real-world applications. This study bridges this research gap by examining data from over 4,000 users in an actual IBTDM program, incenTrip, in the Washington, D.C.-Baltimore region. Employing Poisson-Tweedie generalized additive models to account for excess zeros and nonlinear relations, the study examines how home and work-related factors influence users' enrollment and engagement, measured by the number of registrations, generated trips, and earned incentives at a census block group level. Results reveal that: 1) Urban areas with high population densities and low incomes attract more users and encourage more green travel. 2) Initial enrollment is higher among young, female, Asian, and highly-educated residents, although their subsequent engagement may not be sustained. 3) Workers in educational institutions and retail trades exhibit higher enrollment and maintain stronger engagement than other workers. 4) Well-developed transportation facilities and a higher density of points of interest near the users' homes or workplaces substantially enhance program attractiveness. 5) Nonlinearities, particularly threshold effects, are observed across various relations analyzed. These findings have valuable policy implications for optimizing ongoing IBTDM programs and informing future initiatives. Policy recommendations include implementing targeted and progressive incentives, adopting combined TDM strategies, prioritizing user-friendly designs, fostering collaboration with employers, and employing nuanced policymaking. [ABSTRACT FROM AUTHOR]

Background: Digital programs need to understand the characteristics of their audiences to develop services that meet the needs of different user groups. Focus of the Article: This manuscript is related to research and evaluation, and to four social marketing benchmarks: Consumer Research, Segmentation and Targeting, Consumer Orientation, and Exchange. Research Question: What are the behavioral characteristics and user profiles among duka owners who are using a digital business capacity-building platform in Nairobi, Kenya. Design/Approach: Survey assessment of use of a mobile-friendly online platform for promoting business growth among underserved micro-retail shop (duka) owners in Nairobi, Kenya. The UJoin platform offers duka owners access to business and financial courses, online mentoring, networking opportunities, and access to product information. Importance to the Field: The research will provide critical insights into program and audience needs for use of digital platforms, including promoting, scaling, and strengthening digital services. Methods: 805 shop owners in Nairobi, Kenya participated in a survey about perceptions and use of the internet. Latent class analysis identified homogeneous "classes" within the sample, and behavioral profiles and predictors of platform use. Results: Analysis yielded a 3-class model. Class 1 Endorsers endorsed community norms, social support, learning, networking, and perceived business benefits from websites. Class 2 Skeptics did not support collaboration and learning. Class 3 Unengaged lacked support to use online platforms. Predictors of frequent use of digital platforms were self-efficacy (OR: 5.95, p <.001), Endorser (OR: 3.13, p <.001) and Unengaged (OR: 2.42, p <.055) class, and agreeing that connections to duka owners is important (OR: 3.02, p <.003). Conclusion: Diversified strategies to promote use of online platforms may meet different needs of sub-groups among user groups. Recommendations for Research and Practice: Multiple strategies are needed to address different needs of sub-groups within a larger audience. Programs may benefit from investments to characterize the audience during recruitment to better understand attitudes towards, and efficacy to use, the internet, level of motivation, technology and support needs, and attitudes towards learning and networking. Limitations: The survey sample was a non-random selection of duka owners and relied on self-reported data which may be subject to social-desirability bias and recall. Some of the survey questions about perceptions were derived from single-item variables rather than an index or scale. The cross-sectional design of the survey precludes causal inferences. [ABSTRACT FROM AUTHOR]

Software reliability evaluation of complex systems is always a challenging task with conventional methods comprising both functional as well as nonfunctional aspects of real-world applications. Prevailing model frameworks moreover apply a nonfunctional approach (black-box model) that is modeled on defect data or through a functional approach (white-box model) that uses component or state-based interactions. Also, other challenges involve integrating both approaches, and validating user profiles of software operation. Further, reliability assessment is one among the most important and desirable qualities of service requirements of software systems, particularly in monitoring critical business transactions. Here, we propose a model framework to evaluate the overall reliability estimation involving both functional and nonfunctional model analyses using: (a) white-box assessment based on intercomponent analysis via component-based Cheung's model and user profile validations with one of the identified deep learning techniques and (b) black-box modeling evaluation via generalized stochastic Petri nets based on orthogonal defect classification. A newly introduced deep learning model using white-box analysis is validated with pertinent usage profiles to establish a new trend in artificial neural networks and as well with software reliability estimation. Additionally, we introduce and present a quantitative technique — analytical hierarchy process — to integrate reliability assessment and provide weights to the white-box and as well for black-box approaches to quantify overall reliability estimation. The proposed framework is illustrated with an application case study. [ABSTRACT FROM AUTHOR]

Low socioeconomic status (SES) is associated with a higher prevalence of unhealthy lifestyles compared to a high SES. Health interventions that promote a healthy lifestyle, like eHealth solutions, face limited adoption in low SES groups. To improve the adoption of eHealth interventions, their alignment with the target group's attitudes is crucial. This study investigated the attitudes of people with a low SES toward health, healthcare, and eHealth. We adopted a mixed-method community-based participatory research approach with 23 members of a community center in a low SES neighborhood in the city of Rotterdam, the Netherlands. We conducted a first set of interviews and analyzed these using a grounded theory approach resulting in a group of themes. These basic themes' representative value was validated and refined by an online questionnaire involving a different sample of 43 participants from multiple community centers in the same neighborhood. We executed three focus groups to validate and contextualize the results. We identified two general attitudes based on nine profiles toward health, healthcare, and eHealth. The first general attitude, optimistically engaged, embodied approximately half our sample and involved light-heartedness toward health, loyalty toward healthcare, and eagerness to adopt eHealth. The second general attitude, doubtfully disadvantaged, represented roughly a quarter of our sample and was related to feeling encumbered toward health, feeling disadvantaged within healthcare, and hesitance toward eHealth adoption. The resulting attitudes strengthen the knowledge of the motivation and behavior of people with low SES regarding their health. Our results indicate that negative health attitudes are not as evident as often claimed. Nevertheless, intervention developers should still be mindful of differentiating life situations, motivations, healthcare needs, and eHealth expectations. Based on our findings, we recommend eHealth should fit into the person's daily life, ensure personal communication, be perceived usable and useful, adapt its communication to literacy level and life situation, allow for meaningful self-monitoring and embody self-efficacy enhancing strategies.

Data security in centralized storage needs advancement in privacy standards because of all of the cloud. The data security in the cloud has been well provided by security industries like cloud network security, data center security, distributed security and soon, also their exist numerous techniques to preserve the privacy of cloud users. The earlier methods enforce user privacy by restricting the malicious access from various users. Data authentication is provable access to keep privacy among other standards. However, the privacy of cloud users has been breached on several occasions. To improve cloud security and enforce efficient privacy preservation, a multi-level micro access restriction algorithm has been presented in this paper. The cloud data has been indexed in multiple levels, the data present in each level has been restricted using the profile and set if encryption standards. The user request has been evaluated for its trusted access according to the access grant present in profile data. Similarly, the cloud data has been encrypted with the user key and the key belongs to the data owner. The method estimates micro access trust weight (MATW), which has been used to restrict the user from malicious access and to preserve user privacy. The method improves the performance of cloud security and introduces higher privacy preservation accuracy. [ABSTRACT FROM AUTHOR]

In this paperwe address the need to design adaptive interacting systems for advanced industrial production machines. Modern production systems have become highly complex and include many subsidiary functionalities, thus making it difficult for least skilled human operators interact with them. In this regard, adapting the behavior of the machine and of the operator interface to the characteristics of the user allows a more effective interaction process, with a positive impact on manufacturing efficiency and user’s satisfaction. To this end, it is crucial to understandwhich are the user’s capabilities that influence the interaction and, hence, should be measured to provide the correct amount of adaptation.Moving along these lines, in this paper we identify groups of users that, despite having different individual capabilities and features, have common needs and response to the interaction with complex production systems. As a consequence,we define clusters of users that have the same need for adaptation. Then, adaptation rules can be defined by considering such users’ clusters, rather than addressing specific individual user’s needs.

A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to obtain politician profiles which include their interests, and these can be automatically learned from their speeches. As a politician may have various areas of expertise, one alternative is to use a set of subprofiles, each of which covers a different subject. In this study, we propose a novel approach for this task by using latent Dirichlet allocation (LDA) to determine the main underlying topics of each political speech, and to distribute the related terms among the different topic-based subprofiles. With this objective, we propose the use of fifteen distance and similarity measures to automatically determine the optimal number of topics discussed in a document, and to demonstrate that every measure converges into five strategies: Euclidean, Dice, Sorensen, Cosine and Overlap. Our experimental results showed that the scores of the different accuracy metrics of the proposed strategies tended to be higher than those of the baselines for expert recommendation tasks, and that the use of an appropriate number of topics has proved relevant. [ABSTRACT FROM AUTHOR]

In social aware network (SAN) paradigm, the fundamental activities concentrate on exploring the behavior and attributes of the users. This investigation of user characteristic aids in the design of highly efficient and suitable protocols. In particular, the shilling attack introduces a high degree of vulnerability into the recommender systems. The shilling attackers use the reviews, user ratings and forged user generated content data for the computation of recommendation rankings. The detection of shilling attack in recommender systems is considered to be essential for sustaining their fairness and reliability. In specific, the collaborative filtering strategies utilized for detecting shilling attackers through efficient user behavior mining are considered as the predominant methodologies in the literature. In this paper, a hybrid convolutional neural network (CNN) and long-short term memory (LSTM)-based deep learning model (CNN–LSTM) is proposed for detecting shilling attack in recommender systems. This deep learning model utilizes the transformed network architecture for exploiting the deep-level attributes derived from user rated profiles. It overcomes the limitations of the existing shilling attack detection methods which mostly focuses on identifying spam users by designing features artificially in order to enhance their efficiency and robustness. It is also potent in elucidating deep-level features for efficiently detecting shilling attacks by accurately elaborating the user ratings. The experimental results confirmed the significance of the proposed CNN–LSTM approach by accurately detecting most of the obfuscated attacks compared to the state-of-art algorithms used for investigation. [ABSTRACT FROM AUTHOR]

In this paper a fraud detection method is proposed which user behaviors are modeled using two main components namely the un-normal trend analysis component and scenario based component. The extent of deviation of a transaction from his/her normal behavior is estimated using fuzzy membership functions. The results of applying all membership functions on a transaction will then be infused and a final risk is gained which is the basis for decision making in order to block the arrived transaction or not. An optimized threshold for the value of the final risk is estimated in order to make a balance between the fraud detection rate and alarm rate. Although the assessment of such problems are complicated, we show that this method can be useful in application according to several measures and metrics.

This paper presents an original study with the aim of improving users' performance in completing large questionnaires through adaptability in web forms. Such adaptability is based on the application of machine-learning procedures and an A/B testing approach. To detect the user preferences, behavior, and the optimal version of the forms for all kinds of users, researchers built predictive models using machine-learning algorithms (trained with data from more than 3000 users who participated previously in the questionnaires), extracting the most relevant factors that describe the models, and clustering the users based on their similar characteristics and these factors. Based on these groups and their performance in the system, the researchers generated heuristic rules between the different versions of the web forms to guide users to the most adequate version (modifying the user interface and user experience) for them. To validate the approach and confirm the improvements, the authors tested these redirection rules on a group of more than 1000 users. The results with this cohort of users were better than those achieved without redirection rules at the initial stage. Besides these promising results, the paper proposes a future study that would enhance the process (or automate it) as well as push its application to other fields.

• We introduce an argumentation approach for analysis of Reddit debates. • We map a Reddit debate to a weighted bipartite graph and we define its solution using ideal semantics. • We define graded measures to quantify the relevance of users in a debate. • We propose to use these measures to characterize different user profiles. • We test our approach with real Reddit debates identifying four main user profiles. Online debating forums are important social media for people to voice their opinions and engage in debates with each other. Measuring user relevance on these forums can be useful to identify different user profiles or behaviors in online debates, for example, users that tend to participate at the beginning of a debate and whose comments trigger participation, or users that post relevant comments but are not replied too much. To help users to distinguish such different user profiles, we propose graded measures based on users' influence, the controversy that they generate throughout the debates, their contribution to the polarization of the debates, and their social acceptance, that we extract by analyzing the debates in which the users participate. Our approach is based on an argumentation-based analysis that represents a debate as a valued argumentation framework, in which comments of a debate are arguments, the attack relation between arguments models disagreement between comments, and values for arguments represent the overall support of users for comments. Finally, we test our measures with a sample of users from Reddit debates, identifying four main groups of users, from users with almost no impact on the debate to very active ones with decisive comments for the outcome of the debate. [ABSTRACT FROM AUTHOR]