Subset Of Users Research Articles

SurfaceCast: Ubiquitous, Cross-Device Surface Sharing

Real-time online interaction is the norm today. Tabletops and other dedicated interactive surface devices with direct input and tangible interaction can enhance remote collaboration, and open up new interaction scenarios based on mixed physical/virtual components. However, they are only available to a small subset of users, as they usually require identical bespoke hardware for every participant, are complex to setup, and need custom scenario-specific applications. We present SurfaceCast, a software toolkit designed to merge multiple distributed, heterogeneous end-user devices into a single, shared mixed-reality surface. Supported devices include regular desktop and laptop computers, tablets, and mixed-reality headsets, as well as projector-camera setups and dedicated interactive tabletop systems. This device-agnostic approach provides a fundamental building block for exploration of a far wider range of usage scenarios than previously feasible, including future clients using our provided API. In this paper, we discuss the software architecture of SurfaceCast, present a formative user study and a quantitative performance analysis of our framework, and introduce five example application scenarios which we enhance through the multi-user and multi-device features of the framework. Our results show that the hardware- and content-agnostic architecture of SurfaceCast can run on a wide variety of devices with sufficient performance and fidelity for real-time interaction.

Responses to Mental Health Care Posts by Social Media Influencers: The Moderating Effects of Previous Experience and Follower Involvement

ABSTRACT Mental health information on social media is more often communicated by regular users as opposed to healthcare professionals, and a subset of these regular users, social media influencers, are employed as marketing sources for therapy companies. According to social cognitive theory, an influencer’s story about their positive experiences with therapy should encourage followers to also seek therapy, and stronger involvement with the influencer should enhance these effects. Although research has found that previous experience with modeled behaviors may mute vicarious learning effects, with a nuanced and stigmatized context like mental health treatment, it is important to parse out both the quantity and valence of previous experience in understanding how it shapes audience responses to mental health content on social media. The current study tests a moderated mediation model to help explain the impact of previous viewer experience with therapy (quantity and valence) and involvement with the influencer (similarity and parasocial relationship) on social cognitive theory-related outcomes. Results indicate no interaction effects of the moderators; however, there were direct positive effects of previous viewer experience and SMI outcome on outcome expectations, which in turn positively predicted behavioral intentions to seek therapy. Interestingly, self-efficacy had a negative effect on behavioral intentions.

Association of proton pump inhibitor use with risk of kidney stones: an analysis of cross-sectional data from the US National Health and Nutrition Examination Survey (2007–2018)

ObjectiveSeveral studies have suggested a potential link between use of proton pump inhibitors (PPIs) and the risk of kidney stones, attributed to alterations in urine mineral levels. Our study aimed...

Customized influence maximization in attributed social networks: heuristic and meta-heuristic algorithms

The influence maximization problem is one of the most fundamental topics in social networks. However, most existing studies have focused on non-attributed networks, neglecting the consideration of users’ properties during information propagation. Additionally, specific scenarios may involve external queries that target a particular subset of users, which has not been adequately addressed in prior research. To address these limitations, this study first formulates the customized influence maximization (CIM) problem in the context of attributed social networks. The node score and influence probability are derived by fully considering the user’s attributes and the external queries. Then, we develop two algorithms to identify a group of most influential nodes in CIM. The first is a heuristic algorithm based on discounted degree, which is able to find relatively high-quality solutions in a short time. The second is a meta-heuristic algorithm, which makes several adjustments to the original ant colony algorithm to make it efficient to the CIM problem. Specifically, multiple CIM-related heuristics are derived, and a heuristic adaptation strategy is designed to automatically assign the heuristic information to ants according to the search environments and stages. Extensive experiments show the promising performance of our proposed algorithms in terms of accuracy, efficiency, and robustness.

Digital traces of offline mobilization.

Since 2009, there has been an increase in global protests and related online activity. Yet, it is unclear how and why online activity is related to the mobilization of offline collective action. One proposition is that online polarization (or a relative change in intensity of posting mobilizing content around a salient grievance) can mobilize people offline. The identity-norm nexus and normative alignment models of collective action further argue that to be mobilizing, these posts need to be socially validated. To test these propositions, across two analyses, we used digital traces of online behavior and data science techniques to model people's online and offline behavior around a mass protest. In Study 1a, we used Twitter behavior posted on the day of the protest by attendees or nonattendees (759 users; 7,592 tweets) to train and test a classifier that predicted, with 80% accuracy, who participated in offline collective action. Attendees used their mobile devices to plan logistics and broadcast their presence at the protest. In Study 1b, using the longitudinal Twitter data and metadata of a subset of users from Study 1a (209 users; 277,556 tweets), we found that participation in the protest was not associated with an individual's online polarization over the year prior to the protest, but it was positively associated with the validation ("likes") they received on their relevant posts. These two studies demonstrate that rather than being low cost or trivial, socially validated online interactions about a grievance are actually key to the mobilization and enactment of collective action. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

A richer approach to studying Decision Support System (DSS) interactions is required to understand and predict the nature of actual use in the workplace. We used questionnaire and interview techniques to examine workers’ experiences relating to DSS use in naturalistic settings. We aimed to: 1) Reveal what workers perceive to be the most important factors when deciding whether to accept support from a DSS and 2) Elicit patterns that emerge from DSS users’ recounted experiences using the systems, which may impact their future use. Current and prospective DSS users ( N = 93) from numerous industries responded to a questionnaire relating to the factors they perceive to influence their use of DSSs. Subsequently, a retrospective interview protocol was employed to investigate the experiences of a subset of DSS users ( N = 10). The questionnaire results underscore a range of factors considered to be very important to the acceptance of DSSs (i.e. decision quality; decision importance; decision risk; historical accuracy; decision accountability; and system comprehension). Further, a series of interconnected themes relating to workers’ use of DSSs were identified from the interview transcripts using thematic analysis. We discuss how these issues may impact workers’ intentions to use DSSs in the workplace, and advocate for the use of naturalistic decision-making techniques to study technology acceptance.

937-P: CGM Metrics and HbA1c Are Differentially Associated with Complications and Plasma Metabolites in Long-Duration Type 1 Diabetes

Continuous glucose monitor (CGM)-derived metrics evaluate aspects of glycemic variability that are not assessed with HbA1c alone. However, the comparative contributions of these measures to chronic complications in long-duration Type 1 diabetes (T1D) have not been reported. From the Joslin Medalist Study composed of people with T1D≥50 years, a consecutive 14-day block of CGM data was obtained from a subset of CGM users (n=102) after performing usual activities. In linear regression models, mean historical HbA1c was associated with glucose management indicator (GMI), mean glucose, and time above range (TAR)>250 mg/dL; and inversely with time in range (TIR) 70-180 mg/dL and time below range (TBR)<70 mg/dL (all p<0.001). Medalists with GMI significantly greater (≥0.5%) than mean historical HbA1c also had greater coefficient of variability (p=0.04) and TAR (p=0.01). While retinopathy, nephropathy, and neuropathy were associated with GMI, mean glucose, and TAR (p<0.05), only neuropathy was associated with TBR (p=0.02). In contrast, HbA1c was associated only with self-reported cardiovascular disease in the overall Medalist cohort (n=952; p<0.05). Plasma global metabolomics studies (n=95) revealed that amino acid activation pathways, particularly asparagine and glutamine, were associated with lower TAR and higher TIR; while bile acid synthesis was associated with lower TBR (p<0.05). Meanwhile, amino acid metabolism pathways were associated with lower mean historical HbA1c (p<0.05). Our results suggest that while HbA1c and CGM metrics are closely related in long-duration T1D subjects, they provide differential contributions to the development of complications. Additionally, this is the first study to comprehensively characterize plasma metabolomics with both HbA1c and CGM metrics, which provided novel insights on potential therapeutic targets for glycemic control and variability in T1D, as well as potential protection from complications. Disclosure M.Yu: None. S.Jangolla: None. J.Gauthier: None. N.A.Ziemniak: None. H.Shah: None. G.L.King: Research Support; Janssen Research & Development, LLC. Funding American Diabetes Association (9-18-CVD1-005 to M.C.Y.); Mary K. Iacocca Family Foundation; Thomas J. Beatson, Jr. Foundation

1070-P: Blood Glucose Reduction and Long-Term Sustainability in High-Risk Patients with Type 2 Diabetes over Three Years of Using Digital Platform

Introduction: Long-term management of type 2 diabetes (T2D) and sustaining glycemic control remains a challenge and crucial for reducing clinical complications and subsequent economic costs. This study followed glycemic outcomes, average blood glucose (BG avg) and high readings ratio, over 3 years in persons using a digital therapeutic platform. Objective: To investigate blood glucose (BG) levels in persons with T2D in high-risk who used a digital therapeutic platform to manage their BG levels over three years. Additionally, to compare outcomes between consistently engaged users over 3 years versus less consistent population. Method: A retrospective data analysis was performed on users with T2D in high-risk (baseline BG avg>169 mg/dL in month1; equivalent to A1C 7.5) who activated between 2017-2020 and measured their BG using Dario platform over 3 consecutive years (months 1,6,12,24 and 36). The ratios assessed were BG avg and high BG readings ratio (>180 mg/dL). Digital engagement was assessed by additional parameters including measurement type (fasting/premeal/post meal/bedtime), carbs intake, meal type, and physical activity alongside glucose measurement. Results: A group of 1,239 users significantly reduced their BG avg consistently over 3 years by 15.6% (179±55 vs. 212±42) (p<0.05) and high readings ratio by 39% (p<0.05). A subset of users completed at least one engagement type following months 12, 24, and 36 (N=433) demonstrated significantly greater reductions versus the complementary group (N=806): BG avg by 18.8% (172±51 vs. 212±45) and high readings ratio by 45% (p<0.05). Conclusion: A digital diabetes platform has the potential to affect and sustain glycemic control improvements over 3 years. Moreover, it appears highly likely that engagement to app features in chronic condition management may help users with T2D consistently pay attention to their life behaviors to maintain better clinical outcomes. Disclosure Y.Hershcovitz: Employee; DarioHealth Corp. N.Yaniv: Employee; DarioHealth Corp. S.Budman: Employee; DarioHealth Corp. O.Manejwala: Employee; DarioHealth Corp., Stock/Shareholder; DarioHealth Corp.

Associations between the use of psychedelics and other recreational drugs with mental health and resilience during the COVID-19 pandemic.

The large-scale disruption to peoples' daily lives during the COVID-19 pandemic provides a context for examining whether use of substances such as psychedelics in a naturalistic (outside of a controlled environment) setting, is associated with better mental wellbeing and resilience relative to those who use other drugs, or who do not use drugs at all. We interrogate data from the Great British Intelligence Test and identify that 7.8% out of N = 30,598 unique respondents used recreational drugs inclusive of psychedelics, cannabis, cocaine, and MDMA during the COVID-19 pandemic. Recruitment materials did not mention drug use would be surveyed, thereby enabling us to model the relationship with mood and resilience in people who had not specifically self-selected themselves for a 'drug' study. We report that people form clusters, characterized by different real-world patterns of drug use, and the majority of psychedelics users also use cannabis. However, a subset of cannabis users do not use psychedelics, enabling a subtractive comparison. Those who primarily used psychedelics and cannabis during the COVID-19 pandemic had worse mood self-assessment and resilience scores compared to those who never used drugs or primarily used cannabis. This pattern was also evident for other recreational drug use clusters, except for those who primarily used MDMA and cannabis, who had better mood but were of too low incidence to have confidence in this estimate. These findings cast light on the significant differences in mental wellbeing between users of different drugs and the non-user population during a global-crisis and call for future research to explore the pharmacological, contextual and cultural variables associated with these differences, their generalisability and causal links with greater precision.

Efficient Tree-SVD for Subset Node Embedding over Large Dynamic Graphs

Subset embedding is the task to learn low-dimensional representations for a subset of nodes according to the graph topology. It has applications when we focus on a subset of users, e.g., young adults, and aim to make better recommendations for these target users. In real-world scenarios, graphs are dynamically changing. Thus, it is more desirable to dynamically maintain the subset embeddings to reflect graph updates. The state-of-the-art methods, e.g., DynPPE, still adopt a hashing-based method, while hashing-based solutions are shown to be less effective than matrix factorization (MF)-based methods in existing studies. At the same time, MF-based methods in the literature are too expensive to update the embedding when the graph changes, making them inapplicable on dynamic graphs. Motivated by this, we present Tree-SVD, an efficient and effective MF-based method for dynamic subset embedding. If we simply maintain the whole proximity matrix, then we need to re-do the MF, e.g., truncated Singular Value Decomposition (SVD), on the whole matrix after graph updates, which is prohibitive. To tackle this issue, our main idea is to do hierarchical SVD (HSVD) on the proximity matrix of the given subset, which vertically divides the proximity matrix into multiple sub-matrices, and then repeatedly do SVD on sub-matrices and merge the intermediate results to obtain the final embedding. We first present Tree-SVD, which combines a sparse randomized SVD with an HSVD. Our theoretical analysis shows that our Tree-SVD gains the efficiency of sparse randomized SVD and the flexibility of the HSVD with theoretical guarantees. To further reduce update costs, we present a lazy-update strategy. In this strategy, we only update sub-matrices that changes remarkably in terms of the Frobenius norm. We present theoretical analysis to show the guarantees with our lazy-update strategy. Extensive experiments show the efficiency and effectiveness of Tree-SVD on node classification and link prediction tasks.

Cell-Free UAV Networks: Asymptotic Analysis and Deployment Optimization

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

Does the audience welcome an audience-oriented journalism?

Although the ‘audience turn’ ( Costera Meijer, 2020 ) in journalism has been extensively discussed in studies, and journalists acknowledge the need to improve their relationship with their audience ( Nelson and Lewis 2022 ), the audience’s perspective on journalistic roles remains underexplored ( Riedl and Eberl, 2020 ). Generally, the examination of journalistic roles has been from a production perspective ( Hanusch 2019 ). We aim to address this ‘gap’ by examining journalism as a discursive institution ( Zelizer 1993 ), and focusing on audience-oriented roles, defined in the Worlds of Journalism-study as accommodative roles ( Hanitzsch et al. 2019 ). We surveyed a representative sample (N = 1577) of Flemish respondents about their news use, news attitudes, and their views on audience-oriented journalistic roles. Regression models were used to predict differences in how different subsets of news users view the importance of these journalistic roles. Our findings reveal that subsets of users have diverse expectations of what journalists should do, for instance, younger audiences expect journalists to act less as explainers than older audiences. Incorporating the audience’s views in the discussion of journalistic roles could be a useful approach to strengthen the audience-journalism relationship.

Treatment Patterns, Healthcare Utilization and Clinical Outcomes of Patients with Chronic Obstructive Pulmonary Disease Initiating Single-Inhaler Long-Acting β2-Agonist/Long-Acting Muscarinic Antagonist Dual Therapy in Primary Care in England.

Selection of treatments for patients with chronic obstructive pulmonary disease (COPD) may impact clinical outcomes, healthcare resource use (HCRU) and direct healthcare costs. We aimed to characterize these outcomes along with treatment patterns, for patients with COPD following initiation of single-inhaler long-acting muscarinic antagonist/long-acting β2-agonist (LAMA/LABA) dual therapy in the primary care setting in England. This retrospective cohort study used linked primary care electronic medical record data (Clinical Practice Research Datalink-Aurum) and secondary care administrative data (Hospital Episode Statistics) in England to assess outcomes for patients with COPD who had a prescription for one of four single-inhaler LAMA/LABA dual therapies between 1st June 2015-31st December 2018 (indexing period). Outcomes were assessed during a 12-month follow-up period from the index date (date of earliest prescription of a single-inhaler LAMA/LABA within the indexing period). Incident users were those without previous LAMA/LABA dual therapy prescriptions prior to index; this manuscript focuses on a subset of incident users: non-triple therapy users (patients without concomitant inhaled corticosteroid use at index). Of 10,991 incident users included, 9888 (90.0%) were non-triple therapy users, indexed on umeclidinium/vilanterol (n=4805), aclidinium/formoterol (n=2109), indacaterol/glycopyrronium (n=1785) and tiotropium/olodaterol (n=1189). At 3 months post-index, 63.3% of non-triple therapy users remained on a single-inhaler LAMA/LABA, and 22.1% had discontinued inhaled therapy. Most patients (86.9%) required general practitioner consultations in the first 3 months post-index. Inpatient stays were the biggest contributor to healthcare costs. Acute exacerbations of COPD (AECOPDs), adherence, time-to-triple therapy, time-to-first on-treatment moderate-to-severe AECOPD, time-to-index treatment discontinuation, HCRU and healthcare costs were similar across indexed therapies. Patients initiating treatment with single-inhaler LAMA/LABA in primary care in England were unlikely to switch treatments in the first three months following initiation, but some may discontinue respiratory medication. Outcomes were similar across indexed treatments.

(Private) Kernelized Bandits with Distributed Biased Feedback

In this paper, we study kernelized bandits with distributed biased feedback. This problem is motivated by several real-world applications (such as dynamic pricing, cellular network configuration, and policy making), where users from a large population contribute to the reward of the action chosen by a central entity, but it is difficult to collect feedback from all users. Instead, only biased feedback (due to user heterogeneity) from a subset of users may be available. In addition to such partial biased feedback, we are also faced with two practical challenges due to communication cost and computation complexity. To tackle these challenges, we carefully design a new distributed phase-then-batch-based elimination (DPBE) algorithm, which samples users in phases for collecting feedback to reduce the bias and employs maximum variance reduction to select actions in batches within each phase. By properly choosing the phase length, the batch size, and the confidence width used for eliminating suboptimal actions, we show that DPBE achieves a sublinear regret of ~O(T1-α/2 +√γT T), where α ∈ (0,1) is the user-sampling parameter one can tune. Moreover, DPBE can significantly reduce both communication cost and computation complexity in distributed kernelized bandits, compared to some variants of the state-of-the-art algorithms (originally developed for standard kernelized bandits). Furthermore, by incorporating various differential privacy models (including the central, local, and shuffle models), we generalize DPBE to provide privacy guarantees for users participating in the distributed learning process. Finally, we conduct extensive simulations to validate our theoretical results and evaluate the empirical performance.

D2D Assisted Multi-Antenna Coded Caching

A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity. Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and DL transmission. In the D2D phase, subsets of users are selected to share content with the adjacent users directly. In this regard, a low complexity D2D mode selection algorithm is proposed to find the appropriate set of users for the D2D phase with comparable performance to the optimal exhaustive search. During the DL phase, the base station multicasts the remaining data requested by all the users. We identify scenarios and conditions where D2D transmission can reduce the delivery time. Furthermore, we demonstrate how adding the new D2D phase to the DL-only scenario can significantly reduce the beamformer design complexity in the DL phase. The results further highlight that by partly delivering requested data in the D2D phase, the transmission rate can be boosted due to more efficient use of resources during the subsequent DL phase. As a result, the overall content delivery performance is greatly enhanced, especially in the finite signal-to-noise (SNR) regime.

Privacy-Preserving Federated Learning via Functional Encryption, Revisited

Federated Learning (FL), emerging as a distributed machine learning, is a popular paradigm that allows multiple users to collaboratively train a intermediate model by exchanging local models without the training data leaving each user’s domain. However, FL still suffer from privacy risk such as leaking private information from users’ uploaded local models. To address the privacy concern, several approaches have been proposed to achieve privacy-preserving FL (PPFL) based on differential privacy (DP), multi-party computation (MPC), homomorphic encryption (HE) and functional encryption (FE). Compared with DP, MPC and HE, approaches based on FE is more advantageous and thus becomes the focus of this work. Moreover, all existing PPFL schemes via FE employ a multi-user extension of FE for a specific function, i.e., multi-input FE (MIFE). In this paper, we point out that existing FE-based PPFL schemes have faced with several security issues due to the misuse of MIFE. After reconsidering the security requirements of PPFL, we propose new goals of designing PPFL using FE. To achieve our goals, we propose a new FE called dual-mode decentralized multi-client FE (2DMCFE) and give a concreate construction for 2DMCFE. With 2DMCFE, we propose a new framework of PPFL where we establish a fresh 2DMCFE instance for each subset of users. Security proof shows the strong security of our framework under the semi-honest security setting. Furthermore, experiments conducted on real dataset demonstrate that our framework achieves comparable model accuracy and training efficiency to the basic FE-based scheme while providing stronger security guarantee.

Increasing access to mental health supports for healthcare workers during the COVID-19 pandemic and beyond through a novel coaching program.

The COVID-19 pandemic led to heightened anxiety, distress, and burnout among healthcare workers and faculty in academic medicine. Penn Medicine launched Coping First Aid (CFA) in March 2020 in response to the pandemic. Informed by Psychological First Aid principles and therapeutic micro skills, CFA was designed as a tele-mental healthcare service for health system employees and their families delivered by trained lay volunteer coaches under the supervision of licensed mental health clinicians. We present an overview of the model, feasibility and utilization data, and preliminary implementation and effectiveness outcomes based on cross sectional coach (n = 22) and client (n = 57) self-report surveys with a subset of program users in the first year. A total of 44 individuals completed training and were certified to coach. Over the first 24 months of the program, 513 sessions occurred with 273 clients (119 sessions were no-shows or canceled). Follow-up appointments were recommended in 52.6% (n = 270) of sessions and 21.2% (n = 109) of clients were referred for professional mental health care. Client survey respondents reported CFA was helpful; 60% were very or extremely satisfied, and 74% indicated they would recommend the program. Our preliminary findings suggest that CFA was feasible to implement and most clients found the service beneficial. CFA provides a model for rapidly developing and scaling mental health supports during and beyond the pandemic.

Cannabis, connectivity, and coming of age: Associations between cannabis use and anterior cingulate cortex connectivity during the transition to adulthood.

Cannabis use is common among adolescents and emerging adults and is associated with significant adverse consequences for a subset of users. Rates of use peak between the ages of 18-25, yet the neurobiological consequences for neural systems that are actively developing during this time remain poorly understood. In particular, cannabis exposure may interfere with adaptive development of white matter pathways underlying connectivity of the anterior cingulate cortex, including the cingulum and anterior thalamic radiations (ATR). The current study examined the association between cannabis use during adolescence and emerging adulthood and white matter microstructure of the cingulum and ATR among 158 male subjects enrolled in the Pitt Mother and Child Project, a prospective, longitudinal study of risk and resilience among men of low socioeconomic status. Participants were recruited in infancy, completed follow-up assessments throughout childhood and adolescence, and underwent diffusion imaging at ages 20 and 22. At age 20, moderate cannabis use across adolescence (age 12-19) was associated with higher fractional anisotropy (FA) of the cingulum and ATR, relative to both minimal and heavy adolescent use. Longitudinally, moderate and heavy extended cannabis use (age 12-21) was associated with reduced positive change in FA in the cingulum from age 20 to 22, relative to minimal use. These longitudinal results suggest that cannabis exposure may delay cingulum maturation during the transition to adulthood and potentially impact individuals' functioning later in development.

Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL. Cell-free massive multiple-input–multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time. Aiming to reduce the total execution time of the FL process in CFmMIMO, this article proposes choosing only a subset of available users to participate in FL. An optimal selection of users with favorable link conditions would minimize the execution time of each communication round while limiting the total number of communication rounds required. Toward this end, we formulate a joint optimization problem of user selection, transmit power, and processing frequency, subject to a predefined minimum number of participating users to guarantee the quality of learning. We then develop a new algorithm that is proven to converge to the neighborhood of the stationary points of the formulated problem. Numerical results confirm that our proposed approach significantly reduces the FL total execution time over baseline schemes. The time reduction is more pronounced when the density of access point deployments is moderately low.

Multiple Cooperative Task Allocation in Group-Oriented Social Mobile Crowdsensing

Mobile crowdsensing, a new paradigm, has drawn much attention from the online community, in which mobile users are connected by using smartphones with sharing of information via mobile social networks. Multiple cooperative task allocation (MCTA) is a crucial problem in mobile crowdsensing, where each task requires more than one user to cooperatively complete. As more and more users join sensing tasks in groups, it is indispensable to develop a group-oriented crowdsensing mechanism supporting MCTA. However, existing studies generally focus on a group that can provide sufficient users to accomplish a task. Once these groups no longer exist, the corresponding task will be discarded or be performed with compromised quality. In this article, we propose a novel three-phase approach named Group-oriented Cooperative Crowdsensing (GoCC) to tackle the MCTA problem in social mobile crowdsensing. This approach exploits real-life relationships in the social network to form compatible groups, which improves the task coverage via group-oriented cooperation while achieving good task cooperation quality. Specifically, phase 1 selects a subset of users on the social network as initial leaders and directly pushes sensing tasks to them. Phase 2 utilizes the leaders to search for their socially connected users to model groups. Phase 3 presents the process of group-oriented task allocation for solving the MCTA problem. Experiments on the real-world dataset validate that our approach significantly outperforms the representative approaches.