Heterogeneous Users Research Articles

New Three-Tier Game-Theoretic Approach for Computation Offloading in Multi-Access Edge Computing

In this paper, a new game-theoretic multi-layer computing resource and network resource allocation algorithm is proposed for computation offloading in Multi-access Edge Computing (MEC) networks. We establish a new multi-layer system model for Data Center Operators and develop resource allocation relation between the MEC data center operators and users. We also investigate the matching relationship between the cloudlets and users. Based on the proposed multi-layer allocation algorithm, a joint optimization framework is delivered to realize the allocation of computing resources and communication channels in MEC. Simulation results show that the proposed algorithm provides effective solution for the problem of cloudlets allocation in MEC networks, and can significantly improve the computing and communication capabilities in the presence of user heterogeneity.

How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco

This study adds to the nascent but growing literature on the use of big data for pedestrian route choice analysis. We explore behavioral preferences for various route attributes in Boston, MA using a large dataset of GPS trajectories (n = 11,165) sourced from a third-party smartphone app. Although the data are anonymized and limit our exploration of user heterogeneity, the sample size and area coverage are both much larger than seen in most previous studies. We estimate route choice preferences using a path size logit model, and operationalize the coefficients for policy-making through ‘willingness-to-walk’ measures. The value of these measures is demonstrated through an application of computing pedestrian accessibility to transit stations. Additionally, we compare our findings to a previous study in San Francisco, CA using similar data and methods, and previous literature to explore similarities and differences in pedestrian route choice behavior across major metropolitan areas more generally. While our findings can inform walkability policy and practice on several counts, we recommend future efforts to focus on supplementing this study by surveying hard-to-reach populations for more equitable policy-making.

The Impact of Symbolic Award Introductions on Contribution Behavior in Online Communities

Online communities frequently suffer from the under-contribution of knowledge in terms of both quantity as well as quality. A common method to incentivize contribution behavior by members is the use of non-monetary rewards, more specifically performance contingent symbolic awards. However, a nuanced understanding of how performance contingent symbolic awards affect existing community members that differ in their experience, and by extension propensity to contribute, is currently lacking. In this paper we exploit a natural experiment, analyzing a user-week panel data set of 166,000 observations, spanning 48 weeks and over 3000 community members, and show a negative effect of award introduction on contribution quantity and an insignificant effect on contribution quality on average. Moreover, we are able to show how accounting for heterogeneous user characteristics in terms of within-community experience, govern the effect of award introduction and knowledge accumulation. In particular, we find that low experience members significantly reduce their contribution quality while not changing their contribution amount, and high experience members reduce their contribution quantity without decreasing their contribution quality. Thus, this research contributes to the theory on new forms of organizing, in addition to advancing the literature on symbolic awards and online public goods contributions.

#GirlsWhoRide: Social Media Affordances-In-Practice and Their Role in Organization Creation

Despite growing interest in the role social media plays in entrepreneurial phenomena, how social media users communicate and connect with other users in ways that facilitate such phenomena is little understood. I begin to address this question by using an affordance-in-practice perspective to explore how social media users employ features of the social network site Instagram to communicate and the role of such affordances in the process of entrepreneuring, a processual and relational understanding of organization creation. Drawing on the case of a local group of women motorcyclists which began with one woman’s online quest to find friends with whom to ride and the group’s sudden and rapid expansion into a global organization ten months later, I employ an inductive qualitative content analysis to explore patterns of communication visible in the group’s social media posts and comments by other users leading up to the group’s expansion. Emerging from this inquiry are findings that situated uses of hashtags and social tagging afford heterogeneous users both highly-targeted interpersonal and broadly-interconnective practices of communication which were instrumental in fostering growth of the group’s social media following. I theorize how these affordances-in-practice contribute to organization creation.

Technology Strategy and Competition Track: Thinking Strategically about Network Effects

Many, highly valuable firms such as Alibaba, Alphabet, Amazon, Apple, Meta, and Tencent owe parts of their success to network effects. Therefore, it is unsurprising that scholars have been studying network effects and their implications for decades. However, we still know surprisingly little about (1) how to measure network effects, (2) the implications for firms trying to actively induce network effects, and (3) the competition under heterogeneous network effects. The goal of this symposium is to highlight research that deals with these issues. All four presenters illustrate novel ways to measure the strength of network effects and show how these network effects affect value creation and/or firm performance. Results show that network effects are by no means a panacea for firms as (1) they might be too weak to justify a costly merger, (2) they negatively affect firm performance when paired with incompatible strategies, (3) their strength critically depends on heterogeneous user characteristics, and (4) strong within-period network effects are not enough to achieve a dominant market position without consistently strong inter-period network effects. The four presentations will be followed by a discussion that aims to identify common themes and fruitful avenues for future research on network effects. Dog Eat Dog: Balancing Network Effects and Differentiation in a Digital Platform Merger Presenter: Chiara Farronato; Harvard Business School Network Effects by Choice: A Demand-Side Perspective on Adoption Performance for Board Games Presenter: Joe N. Ploog; UCL School of Management Online Communities on Competing Platforms: Evidence from Game Wikis Presenter: Tobias Kretschmer; LMU Munich How Users Drive Value in Two-sided Markets: Platform Designs That Matter Presenter: Marshall Van Alstyne; Boston U.

Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.

Learning Latent Multi-Criteria Ratings From User Reviews for Recommendations

Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user experiences and highlighting their most valued features. Therefore, researchers have proposed various multi-criteria models to improve recommendation performance. However, most of the existing methods utilize only multi-criteria ratings explicitly provided by the users. Note that explicit multi-criteria ratings are sparse and have the problem of missing values. User reviews, on the other hand, contain richer information of user experiences and reveal multi-dimensional user preferences. Therefore, we propose to use latent multi-criteria ratings generated from user reviews, as opposed to explicit multi-criteria ratings, to provide recommendations and capture latent complex heterogeneous user preferences. Specifically, we propose two novel models for the latent multi-criteria rating generation process: the one-stage model LatentMC-1S that utilizes document hashing method to directly compute latent ratings and the two-stage model LatentMC-2S that uses GRU and Gumbel-Softmax for indirect rating generation. Extensive experiments show that the proposed latent multi-criteria rating approaches outperform explicit ratings across different datasets and performance measures. We also show that latent multi-criteria ratings could be used for imputing missing explicit multi-criteria ratings and thus further improving multi-criteria recommender systems.

The Dial-a-Ride Problem with School Bell Time Adjustment

In this paper, we study a paratransit application in which children are transported every day from their homes to their special schools or medical-social establishments. To optimize this transportation system, the establishments collaborate to propose a joint transportation plan. We propose a new algorithm to jointly build vehicle routes that visit several establishments and simultaneously set the establishments’ opening hours. This algorithm combines a large neighborhood search, the resolution of a route-based model, and the progressive shrinkage of the planning window. It is applied to a real case from the area of Lyon in France, including 34 schools and 575 heterogeneous users served by a heterogeneous fleet of reconfigurable vehicles. On average, we show that in addition to the 10% of saving that can be expected by sharing vehicle routes between schools, 7% of additional savings can be achieved by school bell adjustment. This cost saving also decreases average user ride times and the number of vehicles required, creating longer routes that are more attractive for driver services. Funding: This work was supported by the European Union through the European Regional Development Fund [Grant RA0012500] and by the French Région Auvergne-Rhône-Alpes through the NOMAd project. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1160 .

“We Don’t Meet [Any]where Else, Just Here”: Spatiality of Social Capital in Urban Allotments

Unlike many other types of urban micro‐publics, allotment gardens provide a spatial opportunity for everyday social contact and encounters between heterogeneous user groups who share a common interest. While these micro‐publics have an evidenced capacity for generating social capital, scholars have questioned the extent to which social capital accessed within the allotment garden transcends its physical boundary—and thus the relevance of the micro‐public for social integration by fostering resource transfers between socially‐distant members of the population. In this article, we investigate for whom and to what extent social ties and resources accessed within the garden extend beyond its physical boundary and into other domains of urban life (i.e., scaling resource transfers) in Vantaa, the most multicultural city in Finland. Utilizing a mixed‐methods approach, we integrated crisp‐set qualitative comparative analysis and thematic analysis to explore which configurations of gardener characteristics relate to different resource transfers. We found that although new contacts—including boundary‐crossing contacts—were formed within the micro‐public, they evidenced little potential for scaling resource transfers across social difference, and in some cases even sparked intergroup tensions. These findings illustrate that despite the common interest shared by individuals within this micro‐public, contact between different groups alone is not necessarily sufficient to foster positive social encounters, scaling or otherwise. To improve scaling resource transfers and, more broadly, deepen social connections formed within the micro‐public network, facilitated intercultural dialogue by relevant institutions is needed.

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

MotiLearn: Contract-Based Incentive Mechanism for Heterogeneous Edge Collaborative Training

Due to the computation capacity improvement of edge devices and the popularization of artificial intelligence, there has been a dramatic increase in topic of edge intelligence. In order to motivate edge users to participate in task release and collaborative training, it is crucial to design a feasible incentive mechanism so that the task publisher (Principal) can maximize his interests while guaranteeing the income of co-trainers (Agent). In this paper, we first characterize the computation model, agent's benefits, and principal's profits model by considering collaborative training features and user heterogeneity. Furthermore, through the asymmetry of information, we divide the edge intelligent environment into complete information and incomplete information scenarios, in which the principal only knows the distribution of the agents’ private information rather than the specific information. Subsequently by discussing the above two situations in a static environment, we obtain the basic laws of market operation and reveal the significance of introducing a dynamic environment. Then a two-period contract-based incentive mechanism (MotiLearn) is proposed to overcome the ratchet effects of long-term contracts under dynamic incomplete information environment. Finally, theoretical proof and numerical results show that the proposed incentive mechanism is feasible and can motivate both the principal and agents positively.

Toward Cranfield-inspired reusability assessment in interactive information retrieval evaluation

Re-using research resources is essential for advancing knowledge and developing repeatable, empirically solid experiments in scientific fields, including interactive information retrieval (IIR). Despite recent efforts on standardizing research re-use and documentation, how to quantitatively measure the reusability of IIR resources still remains an open challenge. Inspired by the reusability evaluations on Cranfield experiments, our work proactively explores the problem of measuring IIR test collection reusability and makes threefold contributions: (1) constructing a novel usefulness-oriented framework with specific analytical methods for evaluating the reusability of IIR test collections consisting of query sets, document/page sets, and sets of task-document usefulness (tuse); (2) explaining the potential impacts of varying IIR-specific factors (e.g. search tasks, sessions, user characteristics) on test collection reusability; (3) proposing actionable methods for building reusable test collections in IIR and thereby amortizing the true cost of user-oriented evaluations. The Cranfield-inspired reusability assessment framework serves as an initial step towards accurately evaluating the reusability of IIR research resources and measuring the reproducibility of IIR evaluation results. It also demonstrates an innovative approach to integrating the insights from individual heterogeneous user studies with the evaluation techniques developed in standardized ad hoc retrieval experiments, which will facilitate the maturation of IIR fields and eventually benefits both sides of research.

An Improved Modelling of User Clustering for Small Cell Deployment in Heterogeneous Cellular Network

Users in practical cellular geographical areas are found to be non-uniformly distributed. Small cell (SC) deployments in heterogeneous user distribution in a cellular geographical area help to meet high data rate user demands for multimedia data communications in hot spots. SCs help to offload traffic burden from the macro cell (MC) base station, and also cater the data traffic need for the edge users where signal strength from the MC base station (BS) is very weak. For deployments of SCs along with the central MC BS (hence called HetNet) in such spatial heterogeneous user distribution, effective user grouping or clustering algorithm is required for appropriate and satisfactory service coverage. We call it service grouping or clustering of users to be put under a SC for data transmission and reception. It does not disturb the spatial positions of users in clustered non-uniform distribution. Efficient grouping or clustering of users and then deploying a SC at optimal location enhances the performance of the HetNet. It is found that the K-means algorithm used for such grouping of users to position SCs is not efficient. A novel and improved user grouping algorithm is proposed in this paper which performs much better compared to the k-means algorithm. The proposed algorithm of modelling of user clustering results in increase in the number of users under SCs, increase in more offloading of data traffic from MC BS thereby increasing data throughput of MC users. The algorithm also increases in the energy efficiencies of the SCs which is considered as one important performance metric. A doubly stochastic poison process (DSPP), also called Cox process, is assumed here for simulation of non-uniform user distributions. We consider Rayleigh distributed small scale fading model, large scale fading factor representing shadow fading, and users’ geographical distances from BSs to evaluate users’ data rates.

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction.

With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

Cooperative Task Offloading and Content Delivery for Heterogeneous Demands: A Matching Game-Theoretic Approach

In the mobile edge computing (MEC) networks, the heterogeneous demands can be met through device-to-device (D2D) technology due to the idle local resources from the neighbors. Although the overload of mobile edge computing servers can be relieved by sharing idle local resources among neighbors, the active impact of the cooperation in the close neighbors is ignored in the most existing works. In this paper, we study the mutual cooperation between the users with heterogeneous demands such as the computation tasks or the desired contents, and then propose an innovative D2D-assisted heterogeneous users cooperation framework where two users with different demands can share their idle local resources. The optimization is to minimize the aggregate delay of all users in MEC networks. First, the problem is formulated as a heterogeneous demands based matching game involving D2D matching and edge matching. Next, the multi-round cooperation matching (MRCM) algorithm is proposed to find the stable match of the proposed game. Finally, the simulation results present the validity and effectiveness of our proposed game as well as the proposed algorithm.

Energy-Efficient Coexistence of LiFi Users and Light Enabled IoT Devices

Owing to power limitations and hardware constraints of the Internet of Things (IoT) device, it requires simple, low power, low complex, energy-efficient communication technology. In contrast, LiFi users require high data rates and reliable connectivity. Motivated by the diverse requirements of these heterogeneous users, this paper proposes novel green communication schemes that can be used for the coexistence of LiFi users and light communication (LC) enabled IoT devices under a common LiFi access point. The proposed coexistence schemes utilize the amalgamation of wavelength division multiplexing, OFDMA, Hartley transform based DCO-OFDM (DCO-OFDM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {H}}$ </tex-math></inline-formula> ), null DC element, interleaved subcarrier mapping, modified data sequence to achieve concurrent interference-free, low complex and reliable communication. Additionally, as the multiple access (MA) techniques affect the choice of modulation techniques and overall performance in the system. Therefore, this paper includes an analytical delay and throughput framework to corroborate the decision of an appropriate combination of MA and modulation techniques in the coexistence scheme. This paper presents a comprehensive analysis of the proposed coexistence schemes against conventional DCO-OFDM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {H}}$ </tex-math></inline-formula> coexistence schemes based on various performance metrics. The results suggest that the proposed DL and UL coexistence schemes reduce the complexity and increase the energy efficiency at the user’s and device’s terminals.

User requirements for the design of smart homes: dimensions and goals

The ‘Smart Home’ is a strongly technology-driven field. While user-centered requirements have been reported for specific features, a considerable gap persists for design based on an everyday home context and the social and emotional nature of the home. To address this, we present a user-centered design process to question and expand narrow framings of energy-efficiency and smart control and consider the richness and variety of the domestic context as design space for smart homes. Our three-step investigation employs cultural probing, participatory design fiction, and focus groups to progress from the home context “as-is” towards a blending of values with technological responses. Our findings highlight the home as a complex construct imbued with organically grown practices and individual and collective needs, values, and emotions. Based on empirical, real-user data we present features and system expectations that address this multifaceted overall picture. This paper advises the design process of future smart home solutions in three facets: first, we discuss the value of the design process applied in this study and future possibilities to expand. Second, we show design dimensions, namely time, space, relations, individual factors, and values that allow design for a heterogeneity of users and situations. Third, we derive specific design goals to highlight directions of smart home system design: design for control, low effort, integration, evolvability, identity, sociability, and benefits.

Dynamic Routing of Heterogeneous Users After Traffic Disruptions Under a Mixed Information Framework

This research focuses on reducing traffic congestion using the competing strategies between informed and uninformed drivers. Under a mixed information framework, a navigation app provides within-day route suggestions to informed drivers using predicted information about the time-varying route habits of uninformed drivers. The informed users detour from initially proposed routes to minimize network congestion after traffic disruptions, pushing the system toward optimal equilibrium, while uninformed drivers make day-to-day decisions which push the system toward user equilibrium. Simulations considering varying fractions of informed drivers show that congestion is reduced during abrupt phase transition before reaching equilibrium by approximately 59.2% when 20% of drivers are informed, and is nearly eliminated when 80% of drivers are informed, which could be achieved through connected vehicle technologies. Shared memory multi-core parallelization improved the computational efficiency.

The Effect of Key Indicators on the Operation Costs for Public Toll Roads

At the end of the build-operate-transfer road concession period, an optimal model for the operation of public toll roads is created based on user heterogeneity regarding the values of time for different road users. The impact of user heterogeneity on operation costs for government and private firms is subsequently analyzed on the following critical variables: user values of time, road volume/capacity ratio, and road capacity. Concerning the values of time for different road users, the mean residual and failure functions are established to describe three optimization hypotheses: maximization of social welfare with operation by the government, two extreme cases with operation by a private firm, and a Pareto-optimal solution with operation by a private firm. It is concluded that the mean residual values of the time function are a linear function of the user values of time under a Pareto-optimal operation by the government. It is also determined that private profit is related to the demand-related operational cost of the government and private firm under a Pareto-optimal operation by a private firm. These conclusions suggest relevant recommendations for the government on policymaking for the operation of public toll roads.

FMCPR: Flexible Multiparameter-Based Channel Prediction and Ranking for CR-Enabled Massive IoT

The cognitive radio-enabled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$massive$ </tex-math></inline-formula> Internet of Things (CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT) is envisioned to shape the future of densely connected IoT devices in the sixth-generation networks to support the hyperconnected society. In conventional CR networks, secondary users (SUs) sense the whole block of spectrum to find idle channels, which is an energy-consuming, delay-inducing, and processing-intensive task. With the large scale of resource-constrained heterogeneous devices in CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT, the sensing process becomes a major hurdle for CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT devices to achieve efficient utilization of the limited device and network resources. Thus, a novel multiparameter-based flexible scheme is proposed for idle channel prediction and channel ranking, which considers priorities as well as heterogeneity of users. The scheme uses a probabilistic approach and employs multiple parameters simultaneously to evaluate the suitability of a channel before selecting it for transmission. In addition, valid channel obsolescence, a major problem inherent with channel prediction and ranking, is countered by the proposed scheme. The scheme is evaluated under the impact of variable primary and SUs’ arrivals and under multiple channel failures rates and variable sensing and frame time duration. The proposed scheme is also compared with its own modified version that disregards channel failures, and with the random channel selection approach followed by IEEE 802.22. The overall evaluation is conducted under realistic spectrum sensing. Simulation results show that for different parameter values, the proposed scheme improves the collision probability by 11%–55%, reduces sensing time and energy by 60% and 65%, respectively, and enhances throughput by 4%–70%, and spectrum utilization efficiency by 11%–40%.