In this paper, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation and emotion influence. We then propose a Multi-dimensional Emotion Role Mining model to determine a user's emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user's online emotional status, including emotional macro-micro relations, historical emotion preference, structural factor, temporal factor and emotion change factor. Experiment results on a real-life micro-blog retweeting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%.
Kleinberg proposed a family of small-world networks to explain the navigability of social networks. However, the underlying mechanism driving real networks to be navigable is not yet well understood. In this paper, we model the network formation as a game in which people seek for both high reciprocity and long-distance relationships. We show that the navigable small-world network is a Nash Equilibrium of the game. Moreover, we prove that the navigable small-world equilibrium tolerates collusions of any size and arbitrary deviations of a large random set of nodes, while non-navigable equilibria do not tolerate small group collusions or random perturbations.
The aim of this paper is to propose an innovative grouping approach to enhance the interaction and collaboration among peers by considering the complementary degree of students learning state and their social networks. In order to validate our approach, experiments were designed with a group of students and tested with E-learning system developed using genetic algorithm to produce better grouping results. The outcomes clearly indicate that the proposed approach can generate high heterogeneous grouping results. The technical contribution of this paper can be implemented in different MOOCs platforms with thousands of students, with regard to find peers for collaborative works.
Adaptive Case Management (ACM) has emerged as a key BPM technology for supporting unstructured business process. A key problem in ACM is that case schemas need to be changed to best fit the case at hand. Such changes are ad-hoc, and may result in schemas that do not reflect the intended properties. This paper presents a formal approach, based on the Guard-Stage-Milestone model, for reasoning about which properties of a case schema are preserved after a modification, and describes change operations that are guaranteed to preserve certain properties. The approach supports reasoning about rollbacks. A real-life example illustrates applicability.
Third-party networks collect vast amounts of data about users via web sites and mobile applications. Consolidations among tracker companies can notably increase their individual tracking capabilities, prompting scrutiny by competition regulators. Traditional measures of market share, based on revenue or sales, fail to represent the actual reach of a tracker, especially if it spans both web and mobile. This paper proposes a new approach to measure the concentration of tracking capability, based on the reach of a tracker on popular websites and apps. Our results reveal that tracker prominence and parent-subsidiary relationships have significant impact on accurately measuring concentration.
Special Issue on the Economics of Security and Privacy: Guest Editors Introduction
Fog computing has drawn significant research interest as it focuses on bringing Cloud-based services closer to IoT users. To fully leverage the capabilities of distributed, resource-constrained and heterogeneous Fog nodes, applications that are decomposed into inter-dependent modules, can be deployed orderly over the nodes based on their latency-sensitivity. Here, we propose a latency-aware Application Module management policy for Fog to meet the diverse latency-driven issues of different applications. It aims to ensure QoS in terms of meeting the deadline and optimizes energy usage in Fog. Simulation experiments of the proposed policy, demonstrate meaningful improvement in performance over alternative latency-aware strategies.
Online rating systems are often target of manipulation attacks based on posted unfair ratings. In this paper we propose an iterative algorithm to assess rating scores which leverages information about users and score provenance and takes into account the distances between rating options. We prove convergence of our iterative ranking algorithm. We have implemented and tested our rating method on simulated data and world datasets. The experimental results demonstrate that our model provides realistic rating scores even in the presence of massive amount of unfair ratings and outperforms existing ranking algorithms.
Internet-based Indoor Navigation Service-Oriented Architectures (IIN-SOA) organize signals collected by IoT-based devices to enable a wide range of novel applications indoors, where people spend 80-90% of their time. In this paper, we study the problem of prefetching the most important IoT data blocks from an IIN-SOA to a mobile user u, without knowing us target during navigation. Our proposed Grap (Graph Prefetching) framework, structurally analyzes building topologies to identify important areas that become virtual targets to an online heuristic search algorithm we developed. We have tested Grap with datasets from a real IIN-SOA and found it to be impressively accurate.
Mobile-, edge-, and cloud-computing have the potential to form a computing continuum for disruptive applications. The choice of where in the continuum to execute different functionalities is made at run time, based on context and requirements, with the goal of minimizing latency and battery consumption, and maximizing availability. We propose A3-E, a unified model that exploits the Function-as-a-Service to abstract away the heterogeneity of the continuum. Experiments show that A3-E is capable of dynamically routing the application's requests to the continuum, reducing latency by up to $90$\% when using edge infrastructures, and battery consumption by $74$\% when offloading mobile computation.
We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute the crowdsourced service providers to improve the crowdsourced coverage balance. A novel participation probability model is introduced to estimate the expected number of crowdsourced service providers movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.
We propose in this paper a hybrid approach to improve the design structure of Web service interfaces and fix antipatterns as a combination of both deterministic and heuristic-based methods. The first step consists of a deterministic method using graph partitioning-based technique to split the operations of a large service interface into more cohesive interfaces, each one representing a distinct abstraction. Then, the produced interfaces will be checked using a heuristic-based approach based on the non-dominated sorting genetic algorithm (NSGA-II) to correct potential antipatterns while reducing the interface design deviation to avoid taking the service away from its original design.