Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis

Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability distribution is defined by the model from numerical input variables that are only used for conditioning the distribution of discrete response variables. We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods need only minor adjustments to be applied in this generalized setting. The resulting framework provides natural relational extensions of classical probabilistic models for categorical data. We demonstrate the usefulness of RBN models with numeric input relations by several examples. In particular, we use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature vectors associated with the nodes. A generic learning procedure can be used to obtain a maximum-likelihood fit of model parameters and latent feature values for a variety of models that can be expressed in the high-level RBN representation. Specifically, we propose a model that allows us to interpret learned latent feature values as community centrality degrees by which we can identify nodes that are central for one community, that are hubs between communities, or that are isolated nodes. In a multi-relational setting, the model also provides a characterization of how different relations are associated with each community

A Social Force Model for Adjusting Sensing Ranges in Multiple Sensing Agent Systems

In previous work of multiple sensing agent systems (MSASs), they mainly adjust the sensing ranges of agents by centralized heuristics; and the whole adjustment process is controlled in centralized manner. However, such method may not fit for the characteristics of MSASs where the agents are distributed and decide their activities autonomously. To solve such problem, this paper introduces the social force model for adjusting the sensing ranges of multiple sensing agents, which can make the agents adjust their sensing ranges autonomously according to their social forces to other agents and the sensing objects. Based on the social force model, the coverage and optimization models are presented for both point-type and area-type objects. The presented model can produce appropriate social forces among the sensing agents and objects in MSASs; thereby the system observability and lifetime can be improved

Motor Imagery EEG Classification Based on a Weighted Multi-branch Structure Suitable for Multisubject Data

Objective : Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data. Method : This paper proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets. Result : Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively. Conclusion : It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification

A Social Force Model for Adjusting Sensing Ranges in Multiple Sensing Agent Systems

In previous work of multiple sensing agent systems (MSASs), they mainly adjust the sensing ranges of agents by centralized heuristics; and the whole adjustment process is controlled in centralized manner. However, such method may not fit for the characteristics of MSASs where the agents are distributed and decide their activities autonomously. To solve such problem, this paper introduces the social force model for adjusting the sensing ranges of multiple sensing agents, which can make the agents adjust their sensing ranges autonomously according to their social forces to other agents and the sensing objects. Based on the social force model, the coverage and optimization models are presented for both point-type and area-type objects. The presented model can produce appropriate social forces among the sensing agents and objects in MSASs; thereby the system observability and lifetime can be improved

Complex task allocation for crowdsourcing in social network context

Allocation of complex tasks has attracted significant attention in crowdsourcing area recently, which can be categorized into decomposition and monolithic allocations. Decomposition allocation means that each complex task will first be decomposed into a flow of simple subtasks and then the subtasks will be allocated to individual workers; monolithic allocation means that each complex task will be allocated as a whole, which includes individual-oriented and team formation-based approaches. However, those existing approaches have some problems for real crowdsourcing markets. On the other hand, workers are often connected through social networks, which can significantly facilitate crowdsourcing of complex tasks. Therefore, this thesis investigates crowdsourcing in social network context and presents models to address the typical problems in complex task allocation. The main contributions of this thesis are shown as follows. First, traditional decomposition allocation for complex tasks has the following typical problems: 1) decomposing complex tasks into a set of subtasks requires the decomposition capability of the requesters; and 2) reliability may not be ensured when there are many malicious workers in the crowd. To this end, this thesis investigates the context-aware reliable crowdsourcing in social networks. In our approach, when a requester wishes to outsource a task, a worker candidate’s self-situation and contextual-situation in the social network are considered. Complex tasks can be performed through autonomous coordination between the assigned worker and his contextual workers in the social network; thus, requesters can be exempt from decomposing complex tasks into subtasks. Moreover, the reliability of a worker is determined not only by the reputation of the worker himself but also by the reputations of the contextual workers, which can effectively address the unreliability of transient or malicious workers. Second, traditional individual-oriented monolithic allocation for complex tasks often allocate tasks independently, which has the following typical problems: 1) the execution of one task seldom utilize the results of other tasks and the requester must pay in full for the task; and 2) many workers only undertake a very small number of tasks contemporaneously, thus the workers’ skills and time may not be fully utilized. To this end, this thesis investigates the batch allocation for tasks with overlapping skill requirements. Then, two approaches are designed: layered batch allocation and core-based batch allocation. The former approach utilizes the hierarchy pattern to form all possible batches, which can achieve better performance but may require higher computational cost; the latter approach selects core tasks to form batches, which can achieve suboptimal performance with significantly reducing computational cost. If the assigned worker cannot complete a batch of tasks alone, he/she will cooperate with the contextual workers in the social network. Through the batch allocation, requesters’ real payment can be discounted because the real execution cost of tasks can be reduced, and each worker’s real earnings may increase because he/she can undertake more tasks contemporaneously. Third, traditional team formation-based monolithic allocation for complex tasks has the following typical problems: 1) each team is created for only one task, which may be costly and cannot accommodate crowdsourcing markets with a large number of tasks; and 2) most existing studies form teams in a centralized manner, which may place a heavy burden on requesters. To this end, this thesis investigates the distributed team formation for a batch of tasks, in which similar tasks can be addressed in a batch to reduce computational costs and workers can self-organize through their social networks to form teams. In the presented team formation model, the requester only needs to select the first initiator worker and other team members are selected in a distributed manner, which avoids imposing all team formation computation loads on the requester. Then, two heuristic approaches are designed: one is to form a fixed team for all tasks in the batch, which has lower computational complexity; the other is to form a basic team that can be dynamically adjusted for each task in the batch, which performs better in reducing the total payments by requesters. Forth, current workers are often naturally organized into groups through social networks. To address such common problem, this thesis investigates a new group-oriented crowdsourcing paradigm in which the task allocation targets are naturally existing worker groups but not individual workers or artificially-formed teams as before. An assigned group often needs to coordinate with other groups in the social network contexts for performing a complex task since such natural group might not possess all of the required skills to complete the task. Therefore, a concept of contextual crowdsourcing value is presented to measure a group’s capacity to complete a task by coordinating with its contextual groups, which determines the probability that the group is assigned the task; then the task allocation algorithms, including the allocations of groups and the workers actually participating in executing the task, are designed. In summary, this thesis develops new models to cover the shortages of previous complex task allocation works and designs efficient algorithms to solve the corresponding problems by considering the social network contexts. Experimental results conducted on real-world datasets collected from some representative crowdsourcing platforms show that the presented approaches outperform existing benchmark approaches in previous studies.Doctor of Philosoph

Context-aware reliable crowdsourcing in social networks

There are two problems in the traditional crowdsourcing systems for handling complex tasks. First, decomposing complex tasks into a set of micro-subtasks requires the decomposition capability of the requesters; thus, some requesters may abandon using crowdsourcing to accomplish a large number of complex tasks since they cannot bear such heavy burden by themselves. Second, tasks are often assigned redundantly to multiple workers to achieve reliable results, but reliability may not be ensured when there are many malicious workers in the crowd. Currently, it is observed that the workers are often connected through social networks, a feature that can significantly facilitate task allocation and task execution in crowdsourcing. Therefore, this paper investigates crowdsourcing in social networks and presents a novel context-aware reliable crowdsourcing approach. In our presented approach, the two problems in traditional crowdsourcing are addressed as follows: 1) the complex tasks can be performed through autonomous coordination between the assigned worker and his contextual workers in the social network; thus, the requesters can be exempt from a heavy computing load for decomposing complex tasks into subtasks and combing the partial results of subtasks, thereby enabling more requesters to accomplish a large number of complex tasks through crowdsourcing, and 2) the reliability of a worker is determined not only by the reputation of the worker himself but also by the reputations of the contextual workers in the social network; thus, the unreliability of transient or malicious workers can be effectively addressed. The presented approach addresses two types of social networks including simplex and multiplex networks. Based on theoretical analyses and experiments on a real-world dataset, we find that the presented approach can achieve significantly higher task allocation and execution efficiency than the previous benchmark task allocation approaches; moreover, the presented contextual reputation mechanism can achieve relatively higher reliability when there are many malicious workers in the crowd