Local Membership Research Articles

Research on the Application of Membership System in Market Management: Evidence from China

Under the strong competition in the retail industry, large warehouse membership supermarkets have a stable and large consumer base in the market with strong competitiveness. This paper selects two case studies, Costco, which is the world's leading warehouse membership supermarket, and Freshippo, which has newly emerged in the Chinese market, for comparative analysis to explore the industrial structure behind the membership system in the development process and special innovations made by local membership supermarkets in China to satisfy consumers. The Chinese retail market is significantly different and requires localized transformation and innovation in order to attract local consumers to join. The paper also explores Freshippo's attempts to increase the brand's competitiveness in the face of the enormous convenience of online shopping in China, namely, the innovation of an online + offline model and the addition of several consumer benefits to attract more consumers to join the membership family. Through the integration and analysis of the development strategies of two membership supermarkets, we offer specific advice on the next development of membership supermarkets.

Uncertainty parameter weighted entropy-based fuzzy c-means algorithm using complemented membership functions for noisy volumetric brain MR image segmentation

In this paper, we propose an uncertainty parameter weighted entropy-based fuzzy c-means clustering algorithm for noisy volumetric (3D) brain MR image segmentation using complemented global and spatially constraint local membership functions. Due to inherent noise and intensity inhomogeneity (IIH), the acquired MR images have blurry tissue boundaries. This leads to a situation where uncertainty arises while labeling a pixel/voxel into its proper tissue region. Further, it magnifies in the regions of different tissue boundaries. The proposed algorithm addresses this issue by introducing a class-level uncertainty parameter for each voxel and weightedly incorporating in the fuzzy objective function using complemented global and spatially constraint local fuzzy membership functions. It also incorporates total uncertainties in the 3D image domain by means of Shannon entropy. The complemented local fuzzy membership function estimates the degree of non-association, constraint by the local region-level intensity distribution. This framework allows the algorithm to utilize the spatial intensity distribution both in locally and globally within the image domain and produce more accurate cluster prototypes. We validate the proposed algorithm qualitatively and quantitatively with 10 3D simulated brain MR images, contaminated by noise and intensity inhomogeneity, 4 volumes of clinical real patient 3D brain MR images and a synthetic 3D image with added Rician noise. The simulation results in several evaluation indices indicate its superiority over state-of-the-art algorithms developed in recent past.

Its name suggests it belongs here: The power of a community namesake in decreasing founding rates during industry emergence

This study investigates the power of a community namesake, defined as an organization named after the geographic community, in discouraging potential entrants from entering the local market during industry emergence. I argue that the presence of a community namesake is likely to make potential entrants anticipate stronger competition, because they tend to believe that the local membership proudly claimed by the namesake may drive community stakeholders to reciprocate the namesake with disproportionally more resources. During industry emergence, when there lacks objective information on competitive intensity, the presence of a community namesake can be a sufficient cue of a high local entry barrier that deters potential entrants, resulting in lower founding rates during the post-namesake period at the level of communities. I further predict that the namesakes’ symbolic power should vary depending on their material forms. Material forms that corroborate (i.e. through purpose-built buildings) or make salient (i.e. through visually prominent architectures) the namesakes’ claimed local membership should decrease founding rates to a higher degree. The analysis of the emergence of historical movie theaters in Chicago communities, 1905–1927, provides empirical support to the theorizing.

Medley: A Membership Service for IoT Networks

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wireless ad-hoc network. Our failure detector, named Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead. We adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. This adaptation is non-trivial. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. We also provide optimizations to achieve time-bounded detection, as well as to reduce tail detection times. Via analysis, simulation, and Raspberry Pi deployments, we show that Medley can simultaneously optimize detection time and communication traffic.

IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations.

The Old Toponymy and New Topography of Zion: Utah, Photography, and Daniel George's Series God to Go West

The Old Toponymy and New Topography of Zion: Utah, Photography, and Daniel George's Series <i>God to Go West</i>

Density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy

The density peaks clustering algorithm (DPC) has been widely concerned since it was proposed in 2014. There is no need to specify in advance and only one parameter required. However, some disadvantages are still witnessed in DPC: (1) Requiring repeated experiments for choosing a suitable calculation method of the local density due to the variations in the scale of the dataset, which will lead to additional time cost. (2) Difficulty in finding an optimal cutoff distance threshold, since different parameters not only impact the selection of cluster centers but also directly affect the quality of clusters. (3) Poor fault tolerance of the allocation strategy, especially in manifold datasets or datasets with uneven density distribution. Targetting solutions to these problems, a density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy (LF-DPC) is proposed in this paper. First, to obtain a more balanced local density, two classic local density calculation methods are combined in the algorithm to calculate the local fair density through the optimization function with the smallest local density difference. Second, a robust two stage remaining points allocation strategy is designed. In the first stage, k-nearest neighbors are used to quickly and accurately allocate points from the cluster center. In the second stage, to further improve the accuracy of allocation, a fuzzy k-nearest neighbors membership method is designed to allocate the remaining points. Finally, the LF-DPC algorithm has been experimented based on several synthetic and real-world datasets. The results prove that the proposed algorithm has obvious advantages compared with the other five ones.

A modified fuzzy dual-local information c-mean clustering algorithm using quadratic surface as prototype for image segmentation

Single-valued point can not effectively describe the gray characteristic of the image region, which makes existing fuzzy clustering algorithms difficult to segment image with uneven illumination, weak edges and strong noise. Hence, fuzzy c-means clustering with quadratic polynomial surface as clustering center has become a new method to solve complex image segmentation. Aiming at the shortcoming of current latest spatial fuzzy c-means clustering algorithm with quadratic surface as clustering center, quadratic polynomial surface is firstly introduced into existing widely used fuzzy local information c-means clustering algorithm, and further modify the membership degree of fuzzy local information c-means clustering by means of local membership of neighborhood pixels, a novel fuzzy dual-local information c-means clustering with quadratic polynomial-center clusters is proposed to solve the segmentation problem of complex image in the presence of high noise. Then the convergence of the proposed algorithm is strictly proved by Zangwill theorem and bordered Hessian matrix. Experimental results show that the proposed algorithm has better segmentation performance and anti-noise robustness than existing quadratic polynomial-related QPFCM, widely used FLICM, and multiple state-of-the-art algorithms, meanwhile, it also promotes the development of robust fuzzy clustering-related image segmentation theory.

An Adaptive Smoothness Parameter Strategy for Variational Optical Flow Model

The smoothness parameter is used to balance the weight of the data term and the smoothness term in variational optical flow model, which plays very significant role for the optical flow estimation, but existing methods fail to obtain the optimal smoothness parameters (OSP). In order to solve this problem, an adaptive smoothness parameter strategy is proposed. First, an amalgamated simple linear iterative cluster (SLIC) and local membership function (LMF) algorithm is used to segment the entire image into several superpixel regions. Then, image quality parameters (IQP) are calculated, respectively, for each superpixel region. Finally, a neural network model is applied to compute the smoothness parameter by these image quality parameters of each superpixel region. Experiments were done in three public datasets (Middlebury, MPI_Sintel, and KITTI) and our self-constructed outdoor dataset with the proposed method and other existing classical methods; the results show that our OSP method achieves higher accuracy than other smoothness parameter selection methods in all these four datasets. Combined with the dual fractional order variational optical flow model (DFOVOFM), the proposed model shows better performance than other models in scenes with illumination inhomogeneity and abnormity. The OSP method fills the blank of the research of adaptive smoothness parameter, pushing the development of the variational optical flow models.

The Alliance of Community-Owned Water Services in Europe: Opportunities and Challenges Based on the Irish Perspective

The foundation of the Alliance of Community-Owned Water Services in Europe (ACOWAS-EU), established during the consultation period for the 2020 recast of the European Drinking Water Directive, has shone a new light on community-owned drinking water supplies (CoDWS). CoDWS are drinking water supplies that are administered, managed, and owned by the local community membership that each supply serves. This paper reviews the presence of CoDWS within the five founding regions of ACOWAS-EU—Austria, Denmark, Finland, Galicia in Spain, and Ireland—and the co-operative model structure that underpins the sector. Although the co-operative structure for CoDWS has been prominent since the mid-20th century (and sometimes even earlier), there is a dearth of research into the sector’s importance and existence in an international context. Through a detailed case study, the Irish CoDWS sector (known in Ireland as the group water scheme sector) is analysed in depth, in terms of both its evolution and the opportunities and challenges it faces today. Areas, such as water quality, biodiversity, education, and community-involvement are discussed in particular, providing key learnings that may also be of benefit to the other CoDWS sectors within ACOWAS-EU and further afield.

Global and local community memberships for estimating spreading capability of nodes in social networks

The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408–419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).

A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

Von Pfahlbürgern und Zugvögeln: Kontroversen um das deutsche Heimatrecht im 19. Jahrhundert

Modern nation-state building involved the rise of state citizenship and the decline of local membership that had characterized the pre-industrial world. In German-speaking Central Europe, however, this transition was neither quick nor smooth. Rather, the Heimatrecht (home law or home right), which tied people to their community of origin, remained significant throughout the nineteenth century. Recent scholarship on the concept of Heimat has largely ignored its legal history, assuming a rupture between “old” pragmatic-juridical and “new” sentimental-ideological understandings of the term. This article argues that no such clear separation is feasible. Centering on the founding phase of the German Empire, it discusses how the Heimatrecht was defined in the German states before 1871, why it became a bone of political contention between particularism and Prussia-orientated nationalism, and how it affected the lives of migrants. Finally, the article highlights a paradoxical continuity: modern state citizenship enlarged the legal Heimat, but perpetuated its core dilemma.

Registration as a Border: Shaping the Population at the Local Level in Italy

ABSTRACT The article explores the use and misuse of population registers in Italy. These are statistical and administrative tools historically introduced to monitor the territory and the people who live in it. Currently, however, they are often used as devices for selecting the “deserving” members of the local community by municipal authorities which decide, albeit illegitimately, to tighten the requirements for registration. Municipalities which act in this way pervert the function of registers and establish a special kind of border, an administrative border that excludes individuals from status and rights. By analysing how registration is used as a border in Italy, this article aims to contribute to critical border studies and the debate on urban borders. Registration is framed as a membership status which shapes and designs the population as it produces administrative invisibilisation, increases civic stratification and fosters differential inclusion. By denying enrolment in population registers, a precarisation of local membership is obtained. This causes not only symbolic but also material exclusion, preventing those who are not registered from exercising their rights and, in some cases, putting them at risk of being deported.

Membership motivations for natural conservation tourist attractions

ABSTRACT Tourism-related non-profit organizations rely on donations and financial support from private individuals, frequently through membership subscription schemes. This research investigates what motivates membership in a tourism-based conservation context to examine the transferability of existing research in the arts and cultural context. A post-positivist quantitative online survey with qualitative elements was distributed to members of Zealandia, an urban eco-sanctuary in New Zealand. Results identify membership motivation as a multi-dimensional construct, with altruistic motivations related to the cause at its core and inextricably linked with hedonic motivations representing positive on-site experiences. Egoistic motivations were identified as a supplementary factor only in that free entry as a demonstrable benefit allows access to the experience. The organization itself provided a link between cause and those who value it, positioning itself as a point of belonging and connection that mediates the otherwise intangible connection between people and environment. Through this, members receive a variety of emotional, familial and demonstrable benefits in relation to nature itself, a sense of belonging and contribution, and access to observe the impact of their membership. In doing so, this research illustrates the relevance of organization and cause in encouraging local membership behaviour and continuing support for natural conservation tourist attractions.

Insurgent Group Cohesion and the Malleability of ‘Foreignness’: Al-Shabaab’s Relationship with Foreign Fighters

Foreign fighters can be both an asset and a liability for the groups to which they are recruited. While they can bring with them much sought after skills and resources, they can also create tensions between foreign fighter contingents and local membership. This paper considers Al-Shabaab’s relationship with foreign fighters from the group’s foundational years in the 1990s through to the infamous purge of 2011–2013. By examining Al-Shabaab’s history and narratives around foreign-ness and foreign fighters, this paper finds that foreign fighters have persistently influenced Al-Shabaab’s organizational cohesion and structural integrity.

Opting Out of Transit: How Does Strong Local Autonomy Impact Allocation of Transit Service in a Multi-Jurisdictional Transit Agency? A Historical Case Study Analysis

This study examines how the voluntary nature of local membership in transportation agencies can impact resource allocation, drawing on details from a major US transit agency in a state that lets cities opt in or out of transit agency membership. This study finds significant correlation between local opt-outs and transit service using national data. This study examines the impact opt-outs have on transit resource allocation and decision making over time, their effect on transit service over decades, and equity implications, using historical case study analysis from the Dallas Area Rapid Transit system (DART). This study concludes that authorizing legislation allowing local jurisdictions to opt out of transit districts weakens planning capacity, creates a structure making it difficult to allocate scarce transit dollars based on transit need and social equity goals, instead favoring allocation based on satisfying each municipality.

Local Similarity-Based Fuzzy Multiple Kernel One-Class Support Vector Machine

One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs.

Problems of legal qualification of the violation of the restrictions on commenda and concurrently with other activities

The article attempts to examine the problems of qualification of administrative offences related to corruption, in particular violation of restrictions on combining and concurrently governing the imposition of administrative sanctions under article 172-4 С AO . Differentiated legal approaches to the definition of alignment and a second position, control and composition of the Board, Executive or Supervisory bodies, the Supervisory Council of an enterprise or organization. In particular, this article focuses attention on the conflict of laws aspects of legislation, regulatory requirements concerning the incompatibility of functions of state authorities and local self-government and membership of the Board, revealed a problem of lack of harmonization of the list of certain types of permitted activities for certain categories of officials, which creates ambiguity in law enforcement. Given the number of decisions on Affairs about attraction to administrative responsibility under part 2 of article 172-4 of the CAO, that is, the membership of the Board, other Executive or controlling bodies, or Supervisory Council of an enterprise or organization that has the objective of making a profit, based on the analysis which the author identified signs that may indicate the presence or absence of corpus delicti under article 172-4CAO. Also the article presents the categorization of the most common mistakes in law enforcement practice in the consideration of administrative cases on violation of restrictions on combining and overlapping with other activities.