Heterogeneous Users Research Articles

Combining a Genetic Algorithm and a Fuzzy System to Optimize User Centricity in Autonomous Vehicle Concept Development

The megatrends of individualization and sharing will dramatically change our consumer behavior. The needs of a product’s users will be central input for its development. Current development processes are not suitable for this product development; thus, we propose a combination of a genetic algorithm and a fuzzy system for user-centered development. We execute our new methodological approach on the example of autonomous vehicle concepts to demonstrate its implementation and functionality. The genetic algorithm minimizes the required number of vehicle concepts to satisfy the mobility needs of a user group, and the fuzzy system transfers user needs into vehicle-related properties, which are currently input for vehicle concept development. To present this method, we use a typical family and their potential mobility behavior. Our method optimizes their minimal number of vehicle concepts to satisfy all mobility needs and derives the properties of the vehicle concepts. By integrating our method into the entire vehicle concept development process, autonomous vehicles can be designed user-centered in the context of the megatrends of individualization and sharing. In summary, our method enables us to derive an optimized number of products for qualitatively described, heterogeneous user needs and determine their product-related properties.

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data.

BackgroundThe use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. ObjectiveWe review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. MethodsWe applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject.ResultsIn a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. ConclusionsOur findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.

QoS-Guaranteed Adaptive Modulation and Coding for Wireless Scalable Video Multicast

Multicast is a promising solution to address spectrum scarcity in wireless video streaming, while facing tough challenge in provisioning quality of service (QoS) to heterogeneous users. This article presents an adaptive modulation and coding (AMC) scheme to accommodate both spatial diversity and temporal variation on users’ channel conditions for QoS provisioning in wireless scalable video multicast. The QoS-guaranteed AMC policy optimization is formulated as constrained stochastic optimization based on Markov decision processes. An adaptive policy iteration algorithm is developed to find the optimal AMC policy online without any prior knowledge of users’ channel statistics or intensive calculations. This algorithm is attractive due to adaptability to unknown environments and less run-time computation expenditure. Simulations results demonstrate the effectiveness of the proposed scheme.

Use of social media information sources: a systematic literature review

PurposeThis paper aimed to investigate reading format preference of undergraduate students with regard to various categories of information sources and use of social media information sources for academic and non-academic activities.Design/methodology/approachAn exhaustive literature review was carried out in a very well organised way using total 56 publications which include 48 research papers, four conference papers, two books and two reports, which were selected for analysis on the basis of prescribed data schema and adopting the inclusion and exclusion criteria matching with the research objectives of the study.FindingsUndergraduate students preferred text books in print format for passing the exams, and use of search engines and Internet sites were rated as the most crucial source for the academic information. In particular, Wikipedia, YouTube and Blogs were the most frequently used social media information sources by undergraduate students for their academic activities. However, Facebook and Twitter were most prominently used for social interaction.Research limitations/implicationsThe population of the study was restricted to the homogenous group of users, i.e. undergraduate students to examine their choices and preferences for their academic and non-academic activities. Further research studies may be conducted covering the more diverse and heterogeneous user group of postgraduate students and research scholars of different disciplines.Originality/valueGenerally, many previous research studies depicted users' preference for print and electronic media format without paying special attention on the usage of a variety of information sources by user groups. However, the present study is a value addition to the existing literature which emphasised the use of various categories of information sources, particularly by undergraduate students in print and digital format for different purposes in the digital environment. A major shift from the use of print media to digital media to social media information sources by undergraduate students for their academic and non-academic activities.

Bridging or Bonding: An Organizational Framework for Studying Social Capital in Kindergartens.

The article develops our understanding of social capital by analyzing social capital as an organizational phenomenon. The analysis is based on qualitative data consisting of interviews and documents obtained from six different kindergartens in Norway. Kindergartens are used as a “prism” through which we can understand how social capital is formed—and the mechanisms that shape the development of various forms of networks within welfare organizations. More specifically we look at drop-in kindergartens. The specific purpose of these kindergartens is to provide open and inclusive arenas that promote integration and community. We find that the kindergartens vary in the degree to which they succeed in building bridging forms of networks and communities. Using concepts from organizational theory and Wenger’s (1998) theory of communities of practice, we find that formal organizational factors such as ownership, organizational goals, profiling, location, and educational content impact the formation of bridging forms of social capital. The composition of the user groups and the user groups’ motivation for participating most clearly affect the conditions for community formation. The composition of the user groups is the result of a number of organizational factors and organizational mechanisms. Kindergartens that have a heterogeneous user group, and a user group with a community orientation (Morse 2006), are more successful at creating bridging types of social networks.

Personalized Pareto-improving pricing-and-routing schemes for near-optimum freight routing: An alternative approach to congestion pricing

Traffic congestion constitutes a major problem in urban areas. Trucks contribute to congestion and have a negative impact on the environment due to their size, slower dynamics and higher fuel consumption. The individual routing decisions made by truck drivers do not lead to system optimum operations and contribute to traffic imbalances especially in places where the volume of trucks is relatively high. In this paper, we design a coordination mechanism for truck drivers that uses pricing-and-routing schemes that can help alleviate traffic congestion in a general transportation network. We consider the user heterogeneity in Value-Of-Time (VOT) by adopting a multi-class model with stochastic Origin–Destination (OD) demands for the truck drivers. The main characteristic of the mechanism is that the coordinator asks the truck drivers to declare their desired OD pair and pick their individual VOT from a set of N available options, and guarantees that the resulting pricing-and-routing scheme is Pareto-improving, i.e. every truck driver will be better-off compared to the User Equilibrium (UE) and that every truck driver will have an incentive to truthfully declare his/her VOT, while leading to a revenue-neutral (budget balanced) on average mechanism. This approach enables us to design personalized (VOT-based) pricing-and-routing schemes. We show that the Optimum Pricing Scheme (OPS) can be calculated by solving a nonconvex optimization problem. To improve computational efficiency, we propose an Approximately Optimum Pricing Scheme (AOPS) and prove that it satisfies the aforementioned properties. Both pricing-and-routing schemes are compared to the Congestion Pricing with Uniform Revenue Refunding (CPURR) scheme through extensive simulation experiments where it is shown that OPS and AOPS achieve a much lower expected total travel time and expected total monetary cost for the users compared to the CPURR scheme, without negatively affecting the rest of the network. These results demonstrate the efficiency of personalized (VOT-based) pricing-and-routing schemes.

Investigating the Impacts of Transaction Cost under a Tradable Credit Scheme with Heterogenous Users

In this paper, the impacts of transaction cost are investigated under a tradable credit scheme (TCS) considering user heterogeneity. Under the credit scheme, a certain number of credits are initially distributed among all the travelers for a specific O-D pair, and a link-specific number of credits are charged from travelers using that link. The scheme allows for free trading of the credits among travelers, and both the sellers and buyers need to pay an extra transaction cost, which is associated with trading volume. Travelers in the network are assumed to be heterogenous with a discrete value of time (VOT). For a given tradable credit scheme and discrete VOT set, the combined network user equilibrium (UE) and credit-trading market equilibrium (ME) are formulated as a variational inequality (VI) problem, and the conditions for the uniqueness of the network flow pattern and the credit price at equilibrium are established. A bisection-based trial-and-error method is proposed to solve the proposed VI problems. Based on the simulation results, the computational advantages of the proposed method are demonstrated. Then, an example network is presented to investigate the effect of transaction cost in different kinds of markets. It is found that the implementation of transaction cost can suppress trading volume and either elevate or drive down the equilibrium credit price. Besides, it is also found that users with the lowest VOT suffer the most from the increase in transaction cost, while those with the higher VOT are more likely to experience a reduction in travel cost with the implementation of TCS.

A Taxonomy of Social Errors in Human-Robot Interaction

Robotic applications have entered various aspects of our lives, such as health care and educational services. In such Human-robot Interaction (HRI), trust and mutual adaption are established and maintained through a positive social relationship between a user and a robot. This social relationship relies on the perceived competence of a robot on the social-emotional dimension. However, because of technical limitations and user heterogeneity, current HRI is far from error-free, especially when a system leaves controlled lab environments and is applied to in-the-wild conditions. Errors in HRI may either degrade a user’s perception of a robot’s capability in achieving a task (defined as performance errors in this work) or degrade a user’s perception of a robot’s socio-affective competence (defined as social errors in this work). The impact of these errors and effective strategies to handle such an impact remains an open question. We focus on social errors in HRI in this work. In particular, we identify the major attributes of perceived socio-affective competence by reviewing human social interaction studies and HRI error studies. This motivates us to propose a taxonomy of social errors in HRI. We then discuss the impact of social errors situated in three representative HRI scenarios. This article provides foundations for a systematic analysis of the social-emotional dimension of HRI. The proposed taxonomy of social errors encourages the development of user-centered HRI systems, designed to offer positive and adaptive interaction experiences and improved interaction outcomes.

Iterative Collision Resolution for Slotted ALOHA With NOMA for Heterogeneous Devices

In this paper, the problem of using uncoordinated multiple access (UMA) to serve a massive amount of heterogeneous users is investigated. Leveraging the heterogeneity, we propose a novel UMA protocol, called iterative collision resolution for slotted ALOHA (IRSA) with non-orthogonal multiple access (NOMA), to improve the conventional IRSA. In addition to the inter-slot successive interference cancellation (SIC) technique used in existing IRSA-based schemes, the proposed protocol further employs the intra-slot SIC technique that enables collision resolution for certain configurations of collided packets. A novel multi-dimensional density evolution is then proposed to analyze and to optimize the proposed protocol. Simulation results show that the proposed IRSA with NOMA protocol can efficiently exploit the heterogeneity among users and the multi-dimensional density evolution can accurately predict the throughput performance. Last, an extension of the proposed IRSA with NOMA protocol to the frame-asynchronous setting is investigated, where a boundary effect similar to that in spatially-coupled low-density parity check codes can be observed to bootstrap the decoding process.

Clinical symptoms and personality traits predict subpopulations of treatment-seeking substance users

BackgroundThe heterogeneity of treatment-seeking substance users represents a challenge, as most studies include participants having problems with specific substances or merge polysubstance users into the same category without considering differences between profiles. Considering the inconsistent literature on predictors of treatment outcomes, this study aimed to identify subpopulations of individuals with substance use disorders (SUDs) and analyze the association among class membership, previous relapses, and treatment retention. MethodsThe study recruited a total of 159 participants (mean age = 40.60, SD = 8.70; 85.5% males) from two treatment facilities (outpatient daycare and inpatient residential centers). The baseline assessment gathered lifetime and current substance use, and personality and psychopathology measures. The study performed a latent class analysis to identify subpopulations of substance users and explored predictors of class membership using a multinomial regression analysis. ResultsThe study found six different classes of substance users based on their diagnosis and pattern of substance use: class 1 (6.92% of participants): individuals with cannabis as primary substance, alcohol/cocaine as secondary substance and additional use of stimulants or other drugs; class 2 (30.82%): cocaine as primary substance, alcohol as secondary and additional cannabis use; class 3 (20.13%): alcohol as primary substance, cocaine as secondary and additional cannabis use; class 4 (17.61%): cocaine as primary substance, cannabis as secondary and additional alcohol/other drugs use; class 5 (16.35%): alcohol as primary and cannabis as secondary substance; class 6 (8.18%): heroin as primary substance, cocaine as secondary and additional alcohol use. Several traits and clinical symptoms predicted distinct class memberships. Participants pertaining to class 6 presented the highest number of relapses (M = 2.54, SD = 1.56). ConclusionsThese results have several clinical implications. Belonging to class 6 was associated with a greater number of previous relapses. Also, specific psychopathological symptoms and personality traits may impact SUD treatment response, which may help clinicians to guide initial assessment and treatment allocation.

Feasible Point Pursuit and Successive Convex Approximation for Transmit Power Minimization in SWIPT-Multigroup Multicasting Systems

We consider three wireless multi-group (MG) multicasting (MC) systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of both EH group and single MC group. We formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under certain quality-of-service constraints. The problem may be adapted to the well-known semi-definite program and solved via relaxation of rank-1 constraint. However, this process leads to performance degradation in some cases, which increases with the rank of solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit successive convex approximation method in order to address the rank-related issue. The benefits of proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios.

Interoperable access framework for internet of things backhauled heterogeneous applications

Internet of Things (IoT) provides large-scale interoperable service delegations for heterogeneous users through pervasive access. The interconnection of different devices and services in an on-demand and pervasive manner provides seamless responses for different applications. However, a few interconnected devices are stuck into a resource-constraint problem due to limited access and unavailability. This article proposes an interoperable access framework (IAF) for pervasive application services (PAS) for providing robust end-user solutions. The proposed framework balances the accessibility and semantics and its responses for providing seamless interoperability support. In this framework, transfer learning for different limited and free-flow service accessibility is used. The teaching is recurrent for identifying the unavailability of the resources. The process of serving the requests is achieved through interoperable replacement services. It reduces the impact of resource-constraint in the IoT platform, wherein the semantics between unavailability and accessibility is analyzed flawlessly. The proposed framework's performance is verified using the metrics service delay, responses, unavailability, resource utilization, and successful access.

That’s Not Fair: Tariff Structures for Electric Utilities with Rooftop Solar

Problem definition: Utility regulators are grappling to devise compensation schemes for customers who sell rooftop solar generation back to the grid, balancing environmental interests and the financial interests of utilities, solar system installers, and retail customers. This is difficult: Regulatory changes made in Nevada in 2015 to protect Nevada’s utility induced SolarCity, the market leader in solar systems, to suspend local operations. We show that the choice of utility tariff structure is crucial to achieving socially desirable objectives. Academic/practical relevance: It is important for regulators to understand how tariff structure interacts with social objectives. This has implications for consumers, regulators, and industry. Methodology: We use a sequential game to analyze the regulator’s social welfare maximization problem in a market with a regulated utility; an unregulated, price-setting, profit-maximizing solar system installer; and customers who endogenously determine whether to adopt solar or not, based on utility tariffs, solar prices, and their heterogeneous usage profiles and generation potentials. Results: We illustrate that the effectiveness of tariff structures is not governed simply by the number of free tariff parameters, but by the functions these parameters serve. In particular, an effective tariff must discriminate among customer usage tiers and between customers with and without rooftop solar to achieve socially desirable outcomes. We present a tariff structure with these two characteristics and show how it can be implemented as a simple buy-all, sell-all tariff while retaining its favorable properties. We illustrate our findings numerically using data from Nevada and New Mexico, two states grappling with this issue. Managerial implications: Many utilities in the United States operate tariff structures that are missing at least one of the two identified features. Regulators must overhaul these tariff structures to adequately safeguard all stakeholders.

How Should Artificial Intelligence Explain Itself? Understanding Preferences for Explanations Generated by XAI Algorithms

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers’ preferences for various types and formats of explanations. We discuss theoretically and study empirically people’s preferences for explanations of algorithmic decisions. We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs. loan applications) as well as heterogeneity in users’ cognitive styles. Despite their popularity among academics, we find that counterfactual explanations are not popular among users, unless they follow a negative outcome (e.g., loan application was denied). We also find that users are willing to tolerate some complexity in explanations. Finally, our results suggest that preferences for specific (vs. more abstract) explanations are related to the level at which the decision is construed by the user, and to the deliberateness of the user’s cognitive style.

Analyzing Purchase Decisions Using Dynamic Location Data

Retailers’ efforts to monetize consumer location data are still dominated by naive protocols such as geo-fencing – customizing marketing interactions based solely on the current location of all app users. While past research has noted the inefficiency of such approaches due to ignoring the heterogeneity of app users at any given location, alternative methods to monetize longitudinal consumer mobility data are scant in the literature. In this paper, we extend the methods of Ghose, Li, and Liu (2019) from continuously-measured indoor mobility trajectories to low-granularity urban mobility data. We determine consumers’ daily mobility trajectories, and for each day, we define the distance of each store from consumers’ “anticipated trajectories” by averaging store distance over all observed trajectories in the prior few days. To test our method, we model gas station choice during a six-month period in Staten Island, NY. Our data, also used to infer gas station visits, contain 11.7 million location records on 273 thousand devices observed near selected retailers including gas stations. We supplement our data with station-level daily fuel prices and, controlling for brand intercepts and station loyalty, estimate a conditional logit model to assess how consumers trade off gas prices versus store distance. We find that accounting for consumers’ location histories using our method allows for better out-of-sample predictive accuracy despite the generally strong station loyalty in the market. We also conduct counterfactual simulations, which reveal how the busiest traffic flows affect the geographic structure of competition in gasoline retail.

Dynamics Stability and Optimal Control of Virus Propagation Based on the E-Mail Network

Currently, E-mail has become one of the most common communication methods in daily life, while Email virus constitutes one of the major Internet security threats and has attracted considerable attention. This paper addresses the optimal control problem when encountering out-breaks of E-mail virus. Based on the email network of North University of China, we establish a SEI email virus propagation model, study the influence of the heterogeneity of the email network on the worm virus propagation. We give a detail analysis for the model and the propagation threshold of system is obtained by stability theory, etc. Moreover, the effects of the heterogeneity of user for the spreading of worms are analyzed, and the optimal control theory is used to further study the time-varying control of worms. In final, the stability of system and the effectiveness of optimal control strategy are confirmed by numerical simulations.

Rate-Splitting Multiple Access for URLLC Uplink in Physical Layer Network Slicing With eMBB

In this paper, we investigate the problem of heterogeneous service coexistence in the scope of 5G and beyond (B5G) networks, where multiple ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) users are connected to a common base station (BS), sharing physical network resources. In contrast to the orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) usually adopted in literature, in this work we employ rate splitting multiple access (RSMA) for URLLC transmission, where a URLLC device splits its message into two sub-messages with partial transmission power, which are potentially recovered at the BS by means of successive interference cancellation (SIC). To study the performance of such methods in the presence of eMBB users, we consider both orthogonal and non-orthogonal network slicing approaches to share the network resources between heterogeneous user profiles with diverse requirements. As a result, we show that, in general, RSMA presents an improved performance in terms of sum-rate and reliability, even when transmitting concurrently with eMBB users. Finally, our results also show that the URLLC sum-rate can be increased by properly adjusting the rate splitting factor based on the average signal-to-noise ratio (SNR), not being necessary instantaneous channel state information (CSI).

PMTER-ABE: a practical multi-authority CP-ABE with traceability, revocation and outsourcing decryption for secure access control in cloud systems

Attribute-based encryption (ABE) has evolved as an efficient and secure method for storage of data with fine-grained access control in cloud platforms. In recent years, increasing diversification in the design of ABE schemes has led to significant research in the assimilation of properties like traceability, revocation, and outsourcing decryption. However, most of the recent ABE schemes incorporate few of these properties and hence lack in robustness to adapt with varying demands of cloud systems. In modern ABE designs, the notions of forward and backward secrecy have been introduced to accommodate the delegation of a large number of heterogeneous users in the system. In general, these features are realized under the concept of user revocation. On the other hand, to control malicious users in the system, it is necessary to implement traceability in integration with user revocation. Finally, for resource-constrained users, outsourcing decryption to proxy servers is a viable option. Thus, we propose PMTER-ABE, a practical decentralized multi-authority traceable and efficiently revocable attribute-based cryptosystem with outsourcing decryption advantage. The key features of our cryptosystem are (i) incorporating large attribute universe with highly expressive policies, (ii) integrating forward and backward secrecy under user revocation, (iii) implementing white-box traceability to detect malicious users, and (iv) outsourcing decryption to reduce the computational overhead of decryption on users. We present the formal proofs for correctness, security, and traceability of PMTER-ABE along with performance analysis. The efficiency and usability of PMTER-ABE is shown with practical implementation and experimental results.

Churning while Learning: Maximizing User Engagement in a Recommendation System

Online media platforms such as Spotify, YouTube Music, and NetEase Cloud Music rely on long-term user engagement for revenue generation. The primary operational level under their control is content recommendation (i.e., what content to recommend to various users), where the right recommendation can induce users to further interact with the platform, sign-up for premium memberships, view ads, and more. However, given constantly updating supply (i.e., new content being created) and heterogeneous user behavior, optimal recommendation is challenging. It requires a careful balance between exploration (understanding the efficacy of new content) and exploitation (recommending well understood existing content). Motivated by a real-world impressions level dataset from NetEase Cloud Music, we propose a two-period model for the platform recommendation problem, with the aim of maximizing long term user engagement. Our model captures two key features of this marketplace: (1) supply-side learning (i.e., platform learning the efficacy of new content) and (2) demand-side heterogeneous churning (i.e., different users churning at different rates as a function of both their engagement and their types). We use our two-period model to understand the interplay between churning while learning and how it impacts the long-term user engagement. In addition to characterizing the structure of the optimal learning policy for the platform recommendation problem (i.e., which users to experiment on), we numerically illustrate our key findings on a wide range of parameters. We find that accounting for heterogeneous churning behavior while learning the efficacy of new content can lead to improvements of up to 14% in the long-term user engagement.

Typology of Forest Users in West Usambara Tanzania and Implication to Forest Management

The West Usambara landscape is a complex ecosystem in Tanzania known for its rich biodiversity and livelihood support within and beyond its scope. The community dependency on this forest places pressure on its resources, so this forest requires better management strategies. Previous studies on forest conservation ignored details on the heterogeneity of forest users around the forest fringes. Part of the challenge is to understand the characteristics, behavior, and variability of forest users to plan and inform management decisions. This study is an attempt to assess typologies of forest users, their perceptions, and their motivations for understanding better forest management and utilization. The data were collected from 159 randomly sampled households located in four villages bordering the forests, supplemented with focus group discussions and key informant interviews. A factor analysis identified three management and two utilization dimensions, while cluster analysis identified three typologies: high (HFIS), medium (MFIS), and low (LFIS) forest users. The typologies varied in their socioeconomic characteristics, management, and utilization index. The management and utilization index varied from low for HFIS and MFIS to medium for LFIS, indicating a possible difference in resource utilization as well as incentives for management efforts. A multinomial logit indicated further that age, training, and livestock ownership limited upward trends in forest utilization. These findings indicate that, to improve forest management in West Usambara, different management prescriptions are required to respond to the characteristics and variability of forest users (along typologies). Moreover, forest-linked income-generating activities should be encouraged to improve forest income and motivate villagers’ engagement in the forest activities.