Heterogeneous Users Research Articles

Tradable credit scheme design with transaction cost and equity constraint

In this paper, we study the tradable credit scheme design problem considering transaction cost and social equity. Heterogeneous users with different value of times (VOT) and elastic demand are assumed. The credit scheme is characterized by user anonymous initial endowment and link-specific credit charge, and incorporates transaction cost, which is associated with trading volume and independent of credit price. The equilibrium problem is formulated as a variational inequality (VI) problem and the existence and uniqueness of its solution are guaranteed. Based on the VI formulation, a credit scheme design problem with equity constraint is proposed, aimed at maximizing the total social welfare. Based on the results from the example network, the impacts of transaction cost under various equity constraints are investigated. It is found that the transaction cost can negatively affect travel disutility for low-VOT users, and the imposition of equity constraint can address the inequity.

The role of involvement with public transport in the relationship between service quality, satisfaction and behavioral intentions

Abstract Several studies have made manifest that involvement with public transport play a key role in the intentions of its use. However, conflicting models exist in the literature about involvement’s role in the relationship between service quality, satisfaction and behavioral intentions or loyalty. Previous studies suggest all possible roles: antecedent, mediator, moderator and direct effects. A structural equation modeling approach is applied here to further understand the role of involvement with public transport, comparing eight alternative models and using data from a single survey carried out in five European cities (Madrid, Rome, Berlin, Lisbon and London). Later, the study uses a multiple indicators and multiple causes structural equation modeling approach (SEM-MIMIC) to analyze the effect of heterogeneity present in the data over the four constructs considered (service quality, satisfaction, involvement and behavioral intentions). This comprehensive methodological approach provides a number of noteworthy findings, including the empirical verification that satisfaction is a full mediator between service quality and involvement, and involvement is a full mediator between satisfaction and behavioral intentions. The results further suggest that involvement is the factor that contributes most to behavioral intentions or loyalty, followed by service quality perceptions and satisfaction. Lastly, this study demonstrates the relevance of controlling for heterogeneity in users’ perceptions, so as to obtain more robust relations among factors and identify significant differences among market segments, which could prove useful for public transport operators or policy makers.

Burst Traffic Scheduling for Hybrid E/O Switching DCN: An Error Feedback Spiking Neural Network Approach

Hybrid electrical/optical (E/O) switching data center network (DCN) has recently emerged as a promising paradigm for future DCN architectures. However, there exist two major challenges: 1) the traffic is a mixture of both stable and burst components due to the diverse and heterogeneous user demands; 2) current scheduling algorithms are mostly static and not designed for the complex structure of hybrid E/O switching DCN, provoking frequent burst traffic congestion and performance degradation. This article endeavors to overcome the above challenges as follows. We first construct an error feedback-based spiking neural network (SNN) framework with high accuracy burst traffic prediction. We then design a prediction-assisted scheduling algorithm to handle the worst-case burst traffic. On the one hand, the error feedback-based SNN framework can significantly enhance the extraction of burst traffic features by mimicking the biological neuron system. On the other hand, prediction-assisted scheduling arranges the well-predicted traffic using a global evaluation factor and a traffic scaling factor. The simulation results reveal that our approach can efficiently integrate a spiking neural network into the traffic scheduling scheme and achieve satisfying performance with affordable computational complexity.

Modeling and accomplishing the BEREC network neutrality policy

SummaryNetwork neutrality (NN) is a principle of equal treatment of data in network infrastructures with fairness and universality being the primary outcomes of the NN management practice. For networks, the accomplishment of NN management practice is essential to deal with heterogeneous user requirements and the ever‐increasing data traffic. Current tools and methods address the NN problem by detecting network neutrality violations and detecting traffic differentiation. This paper proposes the NN‐PCM (Network Neutrality Policy Conformance Module) that deploys the BEREC network neutrality policy using a bandwidth allocation model (BAM). The NN‐PCM new approach allocates bandwidth to network users and accomplishes the BEREC NN policy concomitantly. Network neutrality is achieved by grouping users with similar traffic requirements in classes and leveraging the bandwidth allocation model's characteristics. The conceptual analysis and simulation results indicate that NN‐PCM allocates bandwidth to users and accomplishes BEREC network neutrality conformance by design with transparent, nondiscriminatory, exceptional, and proportional management practices.

On the Effective Throughput of Coded Caching With Heterogeneous User Preferences: A Game Theoretic Perspective

Proactive caching has emerged as a promising means to accommodate increased demands for wireless capacity. However, studies of proactive caching usually focus on minimizing the overall load of cache-aided networks. How to calculate each user’s caching gain is still an open problem. In this paper, a two-phase cache-aided network is investigated, in which users with heterogeneous preferences are served by a base station through a shared link. Effective throughput is considered as a performance metric, which describes the reduction in each user’s transmission cost. All possible values of effective throughputs achieved by legitimate caching policies form an achievable domain. It is proved that the achievable domain is a convex set and can be characterized by its boundary. A special type of caching policies, termed uncoded placement absolutely-fair (UPAF) caching, is studied. For the two-user case, games are formulated to allocate effective throughput gains for the two users. For the general multiuser case, a UPAF policy is proposed to organize user cooperation. It is shown that users with more concentrated preferences can obtain higher effective throughputs.

Multi‐resource allocation in cloud data centers: A trade‐off on fairness and efficiency

SummaryFair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi‐resource allocation has come into the spotlight. In fact, fair/efficient multi‐resource allocation has become a fundamental problem in any shared computer system. The widely used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF), which tries to equalize each user's dominant share (share of a user's most highly demanded resource, that is, the largest fraction of any resource that the user has required for a task), but this method may still suffer from significant loss of efficiency (i.e., some resources are underused). This article develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2‐dominant resource fairness (2‐DF). Then, we design a non‐trivial on‐line algorithm to find a 2‐DF allocation and extend this concept to k‐dominant resource fairness (k‐DF).

Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.

BackgroundChatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots.ObjectiveThis study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations.MethodsInteractions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules.ResultsUsers sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules.ConclusionsOverall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.

Voluntary and mandatory provision of common-pool resources with heterogeneous users

Abstract Common-pool resource (CPR) users often face two types of problems to solve: provision and appropriation. Provision problems are often solved through the voluntary or mandatory contribution of users. This paper uses a laboratory experiment to explore how the provision scheme shapes extraction choices by CPR users who are heterogeneous in wealth. Users’ choices are compared under three conditions. In the progressive- and regressive-tax conditions, users’ contribution to the provision of CPR is determined exogenously, and in the third condition, users endogenously choose their contribution level. The standard model of own-payoff-maximization and the behavioral models of inequality aversion and equity theory predict different patterns of choices for rich and poor users under each condition. Experimental results are consistent with the predictions of the inequality-aversion model and clearly refute the predictions of equity theory. Importantly, inequality aversion appears to be motivating choices differently in the exogenous- and endogenous-provision conditions: under the regressive- and progressive-tax conditions, extraction choices are consistent with inequality aversion motives, whereas in the endogenous treatment, contribution decisions are consistent with inequality aversion but extraction choices are not.

Media system dependency and user attachment in social Q&A communities: do active users and lurkers differ?

PurposeThis study aims at exploring users' motives to form attachments within the social Q&A community context and identifying the differences between active users and lurkers when building emotional attachments. By utilizing the media system dependency (MSD) theory, this study investigates into the driving factors of dependency relations (understanding, orientation and play) to user attachments (i.e. attachment to the social Q&A community, attachment to content creators).Design/methodology/approachThe research model is empirically validated by an online questionnaire among users of a social Q&A community. Deriving from the actual behavioral data, the authors divide 262 valid responses into 157 active users and 105 lurkers according to whether they post or not. The partial least squares (PLS) method is exploited to analyze the relationships in the model. In addition, the PLS-based multi-group analysis is conducted for comparing active users and lurkers.FindingsThe empirical results confirm that dependency relations (understanding, orientation and play) significantly influence user attachments. Multi-group analysis suggests that the effect of understanding dependency relations on attachment to content creators is stronger for active users than for lurkers. However, the effect of orientation dependency relations on user attachment is significant for lurkers but not significant for active users.Originality/valueThis study enriches the knowledge of the MSD theory by extending it to the social Q&A community setting. Based on the MSD theory, the relationships between three sides of dependency relations and two types of user attachments are hypothesized in the research model. Besides, the impact of user heterogeneity in building user emotional attachment still lacks consideration. This study is one of the first in the field of comparison studies to compare active users and lurkers in such context, providing a novel contribution in understanding the motivations and emotional responses of different users.

A Novel Hybrid Optimization Algorithm for Scalable Video Coding in an SDN

Scalable Video Coding (SVC) is a powerful solution to video application over heterogeneous networks and diversified end users. In the recent years, works mostly concentrate on transported layers or path for a single layer in the Software-Defined Network (SDN). This paper proposes the Novel Hybrid Optimization Algorithm for Scalable Video Coding (NHO-SVC) based on Genetic Algorithm to select the layer and path simultaneously. The algorithm uses the 0/1 knapsack programming model to set up the model, predicts the network states by the Autoregressive Integrated Moving Average Model (ARIMA), and then, makes decision based on Genetic Algorithm.

Joint Customer/Provider Evolutionary Multi-objective Utility Maximization in Cloud Data Center Networks

Cloud computing has found an important role in cost-effective and elastic network resource allocation community. Most researchers and practitioners have focused on cloud provider side for efficient resource allocation to end-users and to warrant their service-level agreements (SLAs). Due to heterogeneous user demands which request services from cloud data center networks, simultaneous satisfaction of all users to their pre-defined SLA level while making the cloud provider and all of the data centers to be cost-effective in terms of the financial income and energy-efficiency is a challenging issue. To address this problem, in the current work, the efficient joint provider/customer efficient resource allocation has been first formulated as a multi-objective optimization problem with convex constraint set in terms of virtual machine (VM) parameters (e.g., storage, CPU core, memory, etc.). Then, an evolutionary solution based on cooperative co-evolution algorithm has been proposed that can conduct a divide-and-conquer methodology for solving the complex optimization problem. Numerical results show the prominent feature of the method in terms of VM usage effectiveness and overall joint network cost/user SLA satisfaction.

A user-operator assignment game with heterogeneous user groups for empirical evaluation of a microtransit service in Luxembourg

ABSTRACT We tackle the problem of evaluating the impact of different operation policies on the performance of a microtransit service. This study is the first empirical application using the stable matching modelling framework to evaluate different operation cost allocation and pricing mechanisms on microtransit service. We extend the deterministic stable matching model to a stochastic reliability-based one to consider user’s heterogeneous perceptions of utility on the service routes. The proposed model is applied to the evaluation of Kussbus microtransit service in Luxembourg. We found that the current Kussbus operation is not a stable outcome. By reducing their route operating costs of 50%, it is expected to increase the ridership of 10%. If Kussbus can reduce in-vehicle travel time on their own by 20%, they can significantly increase profit several folds from the baseline.

Adaptive variable-size virtual clustering for control channel assignment in dynamic access networks: Design and simulations

Cognitive Radio (CR) technology was introduced to effectively exploit the under-utilization portions of the licensed spectrum. A primary challenge in enabling CR networking is how to coordinate the CR communications, in which CR users need to exchange their control information before data exchange can take place. Providing a reliable coordination mechanism in CR networks (CRNs) is not an easy task because of the dynamic nature of primary radio (PR) activities and the heterogeneity of both spectrum availability and CR users’ distribution. In this paper, we propose a dynamic-size clustering with CSMA/CA-based algorithm to overcome the challenge of achieving reliable coordination in CR networks (CRNs). CR users in our scheme are organized into dynamic size clusters and cooperate in selecting common control channels (CCCs) and use them for cluster coordination. The proposed scheme called Dynamic-hop virtual clustering distributed coordination (DH-VCDC). This scheme provides a dynamic-hop virtual clustering based on the traffic condition of both PR and CR users while achieving a minimum number of common available data channels per cluster, in which a better trade-off between the number of common idle channels per cluster and the cluster size is achieved. DH-VCDC scheme exploits the advantages of using Multi-packet Multi-channel Discontinuous Orthogonal Division Multiplexing (DOFDM) to reduce neighbor discovery time, allow CR users to have several cluster memberships, select multiple CCCs and simplify the inter-cluster coordination. CR users in our scheme are grouped to form virtual clusters each contains at least 2 CCCs (primary and backup CCCs). Three modes of DH-VCDC are presented: one-, two- and three-hop modes. Each CR must continuously monitor both PR and CR traffic loads and accordingly chooses the appropriate DH-VCDC mode. The performance of DH-VCDC is compared with reference schemes, in which significant performance improvement is reported.

Evaluation of the breast cancer care network within the Lazio Region (Central Italy).

Background and objectivesA summary indicator for evaluating the breast cancer network has never been measured at the regional level. The aim is to design treemaps providing a summary description of hospitals (including breast units) and Local Health Units (LHUs) in terms of their levels of performance within the breast cancer network of the Lazio region (central Italy). The treemap structure has an intuitive design and displays information from both general and specific analyses.MethodsPatients admitted to the regional hospitals for malignant breast cancer (MBC) surgery in 2010–2017 were selected in a population-based cohort study.These quality indicators were calculated based on the international guidelines (EUSOMA, ESMO) to assess the performance in terms of volume of activity, surgery procedure, post-surgery assistance and timeliness of medical therapy or radiotherapy beginning. The quality indicators were calculated using administrative health data systematically collected at the regional level and were included in the treemap to represent the surgery or the post-surgery areas of the breast cancer clinical pathway.In order to allow aggregation of scores for different indicators belonging to the same clinical area, up to five evaluation classes were defined using the "Jenks Natural Breaks" algorithm. A score and a colour were assigned to each clinical area based on the ranking of the indicators involved. The analyses were performed on an annual basis, by the LHU of residence and by the hospital which performed the surgical intervention.ResultsIn 2017, 6218 surgical interventions for MBC were performed in the hospitals of Lazio. The results showed a continuous increase of the level of performance over the years. Hospitals showed higher variability in the levels of performance than the LHUs. 36% of the evaluated hospitals reached a high level of performance. An audit of the S. Filippo Neri breast unit revealed incorrect coding of the input data. For this reason, the score for the indicator for the volume of wards was re-calculated and re-evaluated, with a subsequent improvement of the level of performance. Most LHUs achieved at least an average overall level of performance, with 20% of the LHUs reaching a high level of performance.ConclusionsThis is the first attempt to apply the treemap logic to a single clinical network, in order to obtain a summary indicator for the evaluation of the breast cancer care network. Our results supply decision makers with a transparent instrument of governance for heterogeneous users, directing efforts improving and promoting equity of care. The treemaps could be reproduced and adapted for other local contexts, in order to limit inappropriateness and ensure uniform levels of breast cancer care within local areas. The next step is the evaluation of audit and feedback interventions to improve the quality of care and to guarantee homogeneous levels of care throughout the region.

Private and Public IPR Protection in a Vertically Differentiated Software Duopoly

We study the interaction between public and private intellectual property rights (IPR) protection in a duopoly in which software developers offer a product variety of differing quality and compete for heterogeneous users, who have an option to buy a legal version, possibly use an illegal copy, or not buy a product at all. Illegal usage implies violation of IPR and is punishable. A developer may use private IPR protection for his software if the level of piracy is high. An important intermediate step in our analysis addresses firms’ pricing strategies and the analysis of the impact of both private and public IPR protection on these strategies (with monopoly serving as a benchmark case). Last but not least, we make some comparisons with an analogous model based on horizontal product differentiation.

Estimating community feedback effect on topic choice in social media with predictive modeling

Social media users post content on various topics. A defining feature of social media is that other users can provide feedback—called community feedback—to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author’s decision to continue the topic of their previous post. We explore the confounding factors, including author’s topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33% and 14% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14%, depending on the user and the amount of feedback.

Effects of Human Behavior Simulation on Usability Factors of Social Sustainability in Architectural Design Education

While the social sustainability of built environments is an essential aspect of architectural design education, systemic experiments still lack empirical pedagogy. Therefore, factors of social sustainability are hardly reflected in students’ projects seamlessly. To overcome such limitations, this study investigates the applicability and effectiveness of human behavior simulation. To ensure authentic architectural design, the projects were equipped with autonomous, rational anthropomorphic computer agents called virtual users (VUsers). This study compared the performance scores on social sustainability factors, assessed by the students who conducted design projects both before (without) and after (with) using the simulation. A one-way analysis of variance indicated that human behavior simulation promoted the performance of projects with respect to the parameters of accessibility and safety, ergonomic usability for heterogeneous users and supportability of social interactions. However, the simulation was not found to be effective in promoting the physical attractiveness of built environments and in ensuring the completeness of design solutions. Based on previous studies, the present study interpreted the reasons why the operability of VUsers and built environments, representations of emerging interactions of VUsers and whole-and-part analytics promoted explicit experimentation, but the factors of physical attractiveness and completeness were irrelevant to the rational examinations in the use of the simulation.

Scheduling and Power Allocation Dampens the Negative Effect of Channel Misreporting in Massive MIMO

We study the sensitivity of multi-user scheduling performance to channel magnitude misreporting in systems with massive antennas. We consider the round-robin scheduler combined with max-min and waterfilling power controls, respectively. We show that user scheduling combined with power allocation, in general, dampens the negative effect of channel misreporting compared to the purely physical layer analysis of channel misreporting without scheduling. We discover several interesting results. First, we observe a periodicity in rate-loss behavior as the number of misreporting users increases. Second, we find that the waterfilling power control is more robust to channel misreporting compared with max-min power control. Third, for homogeneous users with equal average signal-to-noise ratios (SNRs), channel underreporting is harmful but overreporting is beneficial for max-min power control; the opposite impact is found for waterfilling power control. For heterogeneous users with various average SNRs, however, both underreporting and overreporting harm the system for both power control policies, demonstrating the complex interactions across network layers due to channel misreporting.

Reducing Consumer Uncertainty: Towards an Ontology for Geospatial User-Centric Metadata

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria.

Swipe Right?: Experimental Analyses of App-Based Dating in the Age of Criminal Stigma

ABSTRACT Stigma associated with prior incarceration could make dating difficult. To test this in online dating, an experimental audit used constructed dating app profiles of Black, Latinx, and White males and females. Experimental and control profiles were identical with the exception of a parole disclosure statement in bios of experimental profiles. Surprisingly, the White female profile disclosing parole was the only one to match with significantly fewer users. We analyzed the racial congruence of matches and found that White parole disclosing profiles were more likely to attract White users and Black and Latinx profiles attracted more heterogeneous users regardless of parole disclosure. The interaction between racial and criminal stigma and implications for relationship prospects among the formerly incarcerated are considered. Although the timing of criminal history disclosure matters, criminal stigma is unique in app-based dating, having more negative effects for Whites, which differs from other social domains.