Can Status Inequality Organize Cooperation in Multi-leader Systems? An Evolutionary Theory

Status hierarchies often emerge in small collective task groups. In these groups, clearly defined hierarchies facilitate and stabilize structured cooperative interactions among group members, supporting their evolutionary function in the real world. What the existing research in this field has failed to consider, however, is that cooperation matters in these hierarchies with clear status inequality, as well as in other more realistic, multiple-leader groups with less clear hierarchies. Multi-leadership is ubiquitous but, by definition, flattens status inequality and may, in turn, jeopardize its capacity to sustain cooperation. Leveraging the relationship between multi-leadership and cooperation, our evolutionary game model reveals that hierarchies, in general, promote cooperation in groups with multiple leaders, but these hierarchies only do that up to a point, after which multi-leadership backfires. Accordingly, the model provides not only a theoretical account for how multi-leadership coexists with cooperation but also the conditions under which the coexistence would break.

How Social Influence Processes Generate Cohesion in Task Groups

We introduce a theoretical argument linking group structure to an individual’s cohesion in collectively oriented task groups. We posit that status, the distribution of opinions, and social categories indirectly shape perceptions of cohesion by making individuals working on an uncertain task more or less susceptible to the opinions of others. Specifically, these factors influence how likely one is to succumb to the opinions of others, which in turn influences one’s likelihood of viewing one’s actions as valid or consonant with the expectations of the other members of the group. As this process repeats over time, it accumulates to affect individuals’ expressions of cohesion with group members. Results from a laboratory experiment corroborate this process.

Adhocracy as a Model for Organizing Government Institution in Simplifying Indonesian Bureaucracy

The Presidential Regulation instigated this research to simplify the Bureaucracy with only two positions in government organisations. Accountability, position, work complexity, authority, coordination mechanism, organizing, and work system can induce several problems when this regulation is implemented. This research aims to critically explore the model for organizing government institutions in simplifying Bureaucracy. This research employed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). This research shows that the adhocratic organisation model as the antithesis of Bureaucracy by creating several task groups coordinated by the highest functionary position, reconfiguring the management of functionary position, and applicating business process are recommendations to anticipate problems emerging from simplifying Bureaucracy. But, not all government functions can be made adhocratic, and it is also crucial to understand that the management and leaders need to departmentalise in simplifying Bureaucracy meticulously.

DeepME: Deep Mixture Experts for Large-scale Image Classification

Although deep learning has demonstrated its outstanding performance on image classification, most well-known deep networks make efforts to optimize both their structures and their node weights for recognizing fewer (e.g., no more than 1000) object classes. Therefore, it is attractive to extend or mixture such well-known deep networks to support large-scale image classification. According to our best knowledge, how to adaptively and effectively fuse multiple CNNs for large-scale image classification is still under-explored. On this basis, a deep mixture algorithm is developed to support large-scale image classification in this paper. First, a soft spectral clustering method is developed to construct a two-layer ontology (group layer and category layer) by assigning large numbers of image categories into a set of groups according to their inter-category semantic correlations, where the semantically-related image categories under the neighbouring group nodes may share similar learning complexities. Then, such two-layer ontology is further used to generate the task groups, in which each task group contains partial image categories with similar learning complexities and one particular base deep network is learned. Finally, a gate network is learned to combine all base deep networks with fewer diverse outputs to generate a mixture network with larger outputs. Our experimental results on ImageNet10K have demonstrated that our proposed deep mixture algorithm can achieve very competitive results (top 1 accuracy: 32.13%) on large-scale image classification tasks.

Enhanced Wireless Grid Technologies for the Harmonized Devices in Future IoT Systems

The paper proposes the enhanced wireless grid technologies for the future Internet of Things (IoT) systems. The paper shows a realization of the wireless grid by suitably exploiting the existing wireless Smart Utility Networks (SUN) that is standardized by IEEE 802.15.4g/4e task groups and is certified by Wi-SUN alliance. Medium Access Control (MAC) layer functions that is mainly defined by IEEE 802.15.4e standard and IEEE 802.15.10 recommended practice are effectively modified according to the assumed IoT services and satisfy the requirement of harmonized mesh activities by massive radio devices. In order to realize this function, SUN radio devices that exploit Layer 2 Routing (L2R) control scheme in IEEE 802.15.10 are employed to realize the autonomous mesh management function as well as the multiple service supporting function. The performance is evaluated through the experiments by employing the developed SUN devices as well as simulator evaluations. The paper also proposes novel data retransmission schemes by exploiting the data concatenation functions in IEEE 802.15.10 as well as evaluating its performances by computer simulations and experiments. Consequently, this paper confirms that the obtained results through both simulator evaluations and experiments matches to each other.

Disparities in First Instrumental Activity of Daily Living Difficulty between Black and White Older Adults: Findings from the Advanced Cognitive Training in Independent and Vital Elderly Study

Objectives: Few studies have examined differences in age of onset of first self-reported instrumental activities of daily living difficulty, much less differences by race. Our objective was to determine whether there are differences in the first reported difficulty with IADLs between Black and white older adults. Methods: We analyzed data from N = 1168 participants in the Advanced Cognitive Training in Independent and Vital Elderly (ACTIVE) study. A multiple group discrete-time multiple-event process survival mixture (MEPSUM) model was used to estimate the hazard of incident IADL difficulty in seven IADL task groups. Results: No statistically significant differences were identified in the first reported IADL task group difficulty between Black and white older adults. Discussion: Our findings indicate similar patterns of early IADL difficulty in Black and white older adults, suggesting that previously reported racial disparities in ability to perform IADLs may be attributable to differences in absolute risk, not timing.

An empirical research on the adequacy of task-induced involvement load hypothesis: A case for intentional vocabulary learning

The present study aimed at examining the adequacy of the task-induced involvement load hypothesis in intentional learning. An investigation was carried out to find out whether proficiency level of learners had a role in the effectiveness of the vocabulary tasks with different involvement loads. One hundred and thirty-six university students were randomly assigned into four task groups, each of which included upper and lower intermediate learners. Reading comprehension and discussion, reading comprehension and gap filling, reading comprehension plus sentence-making and reading comprehension plus translation with different involvement loads were compared against each other in terms of the immediate and delayed retention of new words. The study partially supported the involvement load hypothesis in that the task with the highest involvement loads resulted in better immediate and delayed retention of new words. The results of the experiment also showed that tasks with similar involvement loads might not result in similar amounts of vocabulary learning. Keywords: Task-induced involvement, load hypothesis, vocabulary learning, word retention, task.