Based on a Neo-Sprangerian approach to forms of life in Western cultures, and drawing on humanities-based ideas about personality, a critical-hermeneutic description of a neoliberal form of life and ...its corresponding form of subjectivity is presented. In the neoliberal form of subjectivity, the self becomes central, but in a way that the distinction between an ego and the self is no longer relevant. Neoliberal thinking is reduced to utilitarian, calculating thinking in all domains of life from work, to interaction, and to identity. Feeling is considered to be more relevant than thinking and is used to manage stress while aiming for happiness, which is core to this subjectivity. It is argued that agency is reduced to self- and family-interests while consequences for the conduct of life are presented. Concepts such as new nihilism, reduction of individuality, and (im)possibility of resistance in neoliberalism are discussed.
ACO-based clustering for Ego Network analysis Gonzalez-Pardo, Antonio; Jung, Jason J.; Camacho, David
Future generation computer systems,
January 2017, 2017-01-00, Letnik:
66
Journal Article
Recenzirano
The unstoppable growth of Social Networks (SNs), and the huge number of connected users, have become these networks as one of the most popular and successful domains for a large number of research ...areas. The different possibilities, volume and variety that these SNs offer, has become them an essential tool for every-day working and social relationships. One of the basic features that any SN provides is to allow users to group, organize and classify their connections into different groups, or “circles”. These circles can be defined using different characteristics as roommates, workmates, hobbies, professional skills, etc. The problem of finding these circles taking into account the variety, volume and dynamics of these SNs has become an important challenge for a wide number of Computer Science areas, as Big Data, Data Mining or Machine Learning among others. Problems related to pre-processing, fusion and knowledge discovering of information from these sources are still an open question. This paper presents a new Bio-inspired method, based on Ant Colony Optimization (ACO) algorithms, that has been designed to find and analyze these circles. Given any user in a network, the new method is able to automatically determine the different users that compose his/her groups or circles of interest, so the network will be clustered into different components based on the users profiles and their dynamics. This algorithm has been applied to Ego Networks where the node centering the network (called “Ego”) represents the user being studied. In this work two different ACO algorithms, that differ in the source of information used to perform the community finding tasks, have been designed. The first ACO algorithm uses the information extracted from the topology of the network, whereas the second one uses the profile information provided by users. The proposed algorithms are able to detect the different circles in three popular Social Networks: Facebook, Twitter and Google+. Finally, and using several databases from previous SNs, an experimental evaluation of our methods has been carried out to show how the algorithms are currently working.
•Community finding.•Ego Networks.•Ant Colony Optimization algorithm.
Mobile dating applications such as Tinder have exploded in popularity in recent years. On Tinder, impression management begins with a motivation to download the app, the choice of one's profile ...photos and an assessment of the expectations of potential Tinder matches. These processes occur in a technologically mediated environment of reduced cues and increased control, local proximity and a reduced filtering process. My focus in this paper is this first stage of impression management, which consists of both impression motivation and impression construction. Specifically, what are the pre-match impression management practices of Tinder users? I present the results of interviews with Tinder users in the Netherlands. Participants were recruited via a Tinder profile that advertised the study using the University emblem and a brief description. Interview questions focused on user understandings of self-presentation practices and profile construction. The interviews also examined how users evaluated their potential matches. Results show users' motivations for using Tinder range from entertainment to ego-boost to relationship seeking, and these motivations sometimes change over time. Profile photos are selected in an attempt to present an ideal yet authentic self, and chosen as an illustration of not only one's desirability but also of other indicators such as education level. Tinder users 'swipe' not only in search of people they like, but also for clues as to how to present themselves in order to attract others like them. This research offers insight into user experiences and perceptions within the still under-researched area of inquiry.
The hypervolume metric is widely used to guide the search in multiobjective optimization. However, in parallel expensive multiobjective optimization, the hypervolume-based multipoint expected ...improvement (EI) suffers from high computational overhead and scales poorly with the batch size. To address this issue, we integrate hypervolume-based EI with the MOEA/D framework and propose a novel EI, named the expected direction-based hypervolume improvement (DirHV-EI). The DirHV-EI only measures the hypervolume improvement within each axis-parallel box induced by the modified Tchebycheff scalarization. Thus, it has a simple analytical expression that can be easily computed. Theoretical analysis indicates that the maximization of our proposed improvement function can help to maximize both the weighted hypervolume and the Tchebycheff improvement metrics. Using DirHV-EI, we design a decomposition-based Bayesian optimization algorithm for solving expensive multiobjective optimization problems. At each iteration, the MOEA/D is used to maximize the DirHV-EI values with respect to a number of direction vectors in a collaborative manner, and a number of candidate solutions can be obtained. Then, a submodularity-based greedy selection strategy is used to select multiple query points from the candidates. Experimental results on both benchmark instances and real-world problems show that our proposed algorithm is an efficient and effective method for parallel expensive multiobjective optimization.
We examine how organizations can challenge institutions that are coercively protected by powerful elites-guarded institutions-when they are unable or unwilling to advocate publicly against them. To ...do so, we draw on an in-depth qualitative study of efforts to combat child marriage in Indonesia. We explore how an international children's rights organization worked alongside local nongovernmental organizations and activists to disrupt the institution of child marriage through two discrete strategies: the crafting of an alter ego that takes the appearance of a social movement that has emanated from the grassroots but is actually highly organized, and the use of this alter ego to support the incubation of public dissent by means of a high-stakes event. We contribute to the literature by developing a theorized account of how organizations can challenge guarded institutions when they cannot speak out-an important organizational problem that has received limited attention. We also challenge the theoretical distinction that has been drawn between the organizational mobilization of activists, often referred to as astroturfing, and seemingly organic mobilization that is said to emerge at the grassroots level.
Well-known predictors of prejudice toward Muslims include social dominance and authoritarianism. However, a gap exists for variables reflecting a rejection or mitigation of ideological motivations ...associated with prejudice toward Muslims. We examined if quiet ego was related to positive attitudes toward Muslims, and whether this could be explained by lower levels of authoritarianism, social dominance, and the motivation to express prejudice. We explored this possibility across two studies of adults in the United States (
N
= 376;
N
= 519). In Study 1, regression results showed quiet ego was directly associated with positive attitudes toward Muslims. Study 2 utilized path analyses and found that the direct relationship between quiet ego and positive attitudes toward Muslims was explained by associations between quiet ego and lower endorsement of authoritarianism, social dominance, and the internal motivation to express prejudice toward Muslims. Moreover, these associations held when accounting for several correlates of intergroup attitudes.
This article traces the organization of corruption in public procurement, by theoretically and empirically assessing the contribution of extra‐legal governance organizations (EGO) to supporting it. ...Theoretically, we explore the governance role played by organized criminal groups in corruption networks, facilitating corrupt transactions by lowering search costs, bargaining costs, and enforcement cots. Empirically, the analysis exploits a rare empirical setup of proven cases of both EGO presence and absence in contract awards by Italian municipalities. We use traditional regression and supervised machine‐learning methods for identifying and validating proxy indicators for EGO presence in public procurement such as single bidding or municipal spending concentration. Internal validity of our models is very high, 85% of unseen contracts are correctly classified. External validity is moderate, our predicted EGO presence score correlates with established indicators of organized criminality across the whole of Italy and Europe with a linear correlation coefficient of about 0.4.
Ego-motion estimation plays a critical role in autonomous driving systems by providing accurate and timely information about the vehicle’s position and orientation. To achieve high levels of accuracy ...and robustness, it is essential to leverage a range of sensor modalities to account for highly dynamic and diverse scenes, and consequent sensor limitations.
In this work, we introduce TEFu-Net, a Deep-Learning-based late fusion architecture that combines multiple ego-motion estimates from diverse data modalities, including stereo RGB, LiDAR point clouds and GNSS/IMU measurements. Our approach is non-parametric and scalable, making it adaptable to different sensor set configurations. By leveraging a Long Short-Term Memory (LSTM), TEFu-Net produces reliable and robust spatiotemporal ego-motion estimates. This capability allows it to filter out erroneous input measurements, ensuring the accuracy of the car’s motion calculations over time. Extensive experiments show an average accuracy increase of 63% over TEFu-Net’s input estimators and on par results with the state-of-the-art in real-world driving scenarios. We also demonstrate that our solution can achieve accurate estimates under sensor or input failure. Therefore, TEFu-Net enhances the accuracy and robustness of ego-motion estimation in real-world driving scenarios, particularly in challenging conditions such as cluttered environments, tunnels, dense vegetation, and unstructured scenes. As a result of these enhancements, it bolsters the reliability of autonomous driving functions.
Prior research has found both similar and different effects of self-regulatory resource depletion and cognitive load. To resolve these seeming contradictions, we experimentally compared the effects ...of cognitive load and self-regulatory depletion. Ego depletion led participants to pay more attention to pain and to persist less on a pain test, whereas load had opposite effects (Study 1). Load distracted people from processing and reacting to negative emotional content of pictures (Study 2), and boosted positive feelings even without an overt emotion induction (Study 3), whereas depletion did not change how people felt relative to control. Depletion and load had equivalent null effects on visual recognition memory (Study 2) but different effects on semantic processing involving emotional connections (Study 3). Taken together, results suggest that load distracts attention away from, whereas ego depletion undermines top-down control over the processing of pain and negatively-valenced content. We discuss implications for learning and instruction.
•We empirically compared the effects of cognitive load and depletion.•Load, but not depletion, decreased negative and boosted positive feelings.•Both had null on visual memory but different effects on semantic processing.•Cognitive load distracts away from negative physical and emotional feelings.•Ego depletion undermines top-down control over implicit emotional processes.
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