Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis

3D face scans have been widely used for face modeling and analysis. Due to the fact that face scans provide variable point clouds across frames, they may not capture complete facial data or miss point-to-point correspondences across various facial scans, thus causing difficulties to use such data for analysis. This paper presents an efficient approach to representing facial shapes from face scans through the reconstruction of face models based on regional information and a generic model. A new approach for 3D feature detection and a hybrid approach using two vertex mapping algorithms, displacement mapping and point-to-surface mapping, and a regional blending algorithm are proposed to reconstruct the facial surface detail. The resulting models can represent individual facial shapes consistently and adaptively, establishing facial point correspondences across individual models. The accuracy of the generated models is evaluated quantitatively. The applicability of the models is validated through the application of 3D facial expression recognition using the static 3DFE and dynamic 4DFE databases. A comparison with the state of the art has also been reported.     

Uniform parallel-machine scheduling to minimize makespan with position-based learning curves

Scheduling with learning effects has become a popular topic in the past decade; however, most of the research focuses on single-machine problems. In many situations, there are machines in parallel and the skills of workers might be different due to their individual experience. In this paper, we study a uniform parallel machine problem in which the objective is to jointly find an optimal assignment of operators to machines and an optimal schedule to minimize the makespan. Two heuristic algorithms are proposed and computational experiments are conducted to evaluate their performance.     

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.     

An empirical evaluation of multi-media based learning of a procedural task

The present study investigated the effects of multi-media modules and their combinations on the learning of procedural tasks. In the experiment, 72 participants were classified as having either low- or high spatial ability based on their spatial ability test. They were randomly assigned to one of the six experimental conditions in a 2 × 3 factorial design with verbal modality (on-screen text procedure vs. auditory procedure) and the format of visual representation (static visual representation vs. static visual representation with motion cues vs. animated visual representation). After they completed their learning session, the ability to perform the procedural task was directly measured in a realistic setting. The results revealed that: (1) in the condition of static visual representation, the high spatial ability group outperformed the low spatial ability group, (2) for the low spatial ability participants, the animated visual representation group outperformed the static visual representation group, however, the static visual representation with motion cues group did not outperform the static visual representation group, (3) the use of animated visual representation helped participants with low spatial ability more than those with high spatial ability, and (4) a modality effect was found for the measure of satisfaction when viewing the animated visual representation. Since the participants with low spatial ability benefited from the use of animation, the results might support an idea that people are better able to retrieve the procedural information by viewing animated representation. The findings also might reflect a preference for the auditory mode of presentation with greater familiarity with the type of visual representation.     

I became an attractive person in the virtual world: Users’ identification with virtual communities and avatars

A number of studies have examined virtual worlds, which can facilitate knowledge sharing, education, and enjoyment, among others. However, no study has provided an insightful research model for evaluating virtual worlds. This study suggests that users’ identification with virtual communities and avatars plays a critical role in the construction of attractive virtual worlds. The proposed model measures the level of the user’s identification with virtual communities, through which the user builds his or her trust in other community members. In addition, the study suggests that users’ identification with avatars is an important element of their satisfaction with virtual worlds. The results indicate that users’ identification with virtual communities as well as avatars can enhance their efficacy and trust and thus facilitate their sustained use of virtual services. The results have important theoretical and practical implications.     

The role of self-construal in consumers’ electronic word of mouth (eWOM) in social networking sites: A social cognitive approach

The current study reconceptualized self-construal as a social cognitive indicator of self-observation that individuals employ for developing and maintaining social relationship with others. From the social cognitive perspective, this study investigated how consumers’ self-construal can affect consumers’ electronic word of mouth (eWOM) behavior through two cognitive factors (online community engagement self-efficacy and social outcome expectations) in the context of a social networking site. This study conducted an online experiment that directed 160 participants to visit a newly created online community. The results demonstrated that consumers’ relational view became salient when the consumers’ self-construal was primed to be interdependent rather than independent. Further, the results showed that such interdependent self-construal positively influenced consumers’ eWOM behavioral intentions through their community engagement self-efficacy and their social outcome expectations.     

Getting closer and experiencing together: Antecedents and consequences of psychological distance in social media-enhanced real-time streaming video

Social media services such as YouTube and Flickr have become online necessities for millions of users worldwide. Social media are online services that enable users to share contents, opinions, and perspectives that support communication with other users. Social media places an emphasis on the shared experience between users, which we call co-experience. However, the online characteristics of social media increase psychological distance between users, which, in turn, results in a decrease in the quality of co-experience. Hence, as the goal of this study, we theoretically modeled and empirically verified the antecedents and user experience-based consequences of psychological distance in a social media-enhanced real-time streaming video service. In order to reduce psychological distance, we introduced two system elements: inhabited space (the degree of being situated in context and in a meaningful place) and isomorph effects (the degree of preserving the structure of a user’s actions). We constructed a social media-enhanced real-time streaming video service prototype and conducted a field experiment with actual social media users. The prototype, which streamed a live baseball game, enabled users to simultaneously view the game from remote locations and to interact with each other through cheering tools. The results indicate that inhabited space and isomorph effects reduce psychological distance between users, and this, in turn, enhances co-experience. This paper ends with theoretical as well as practical implications of the study.     

Performance impact of new product development processes for distinct scenarios under different supplier–manufacturer relationships

Although supplier involvement in new product development (NPD) projects has become increasingly important for strengthening a firm's competitive position, few studies have investigated the impact of changing research and development (R&D) workloads in NPD. According to the level of design-related characteristics including design-related communication and design-related nature, we identify four types of supplier–manufacturer relationships: sequential mode, passive supplier involvement, active supplier involvement, and strategic development. Differing from qualitative and survey-oriented research, this study proposes a system dynamics model for the quantitative exploration of workload impacts on R&D-system equilibrium under different supplier–manufacturer relationships. We justify experimentally the NPD performances of these supplier–manufacturer relationship configurations under workload impacts and provide managerial insights.     

A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks

Reliable routing of packets in a Mobile Ad Hoc Network (MANET) has always been a major concern. The open medium and the susceptibility of the nodes of being fault-prone make the design of protocols for these networks a challenging task. The faults in these networks, which occur either due to the failure of nodes or due to reorganization, can eventuate to packet loss. Such losses degrade the performance of the routing protocols running on them. In this paper, we propose a routing algorithm, named as learning automata based fault-tolerant routing algorithm (LAFTRA), which is capable of routing in the presence of faulty nodes in MANETs using multipath routing. We have used the theory of Learning Automata (LA) for optimizing the selection of paths, reducing the overhead in the network, and for learning about the faulty nodes present in the network. The proposed algorithm can be juxtaposed to any existing routing protocol in a MANET. The results of simulation of our protocol using network simulator 2 (ns-2) shows the increase in packet delivery ratio and decrease in overhead compared to the existing protocols. The proposed protocol gains an edge over FTAR, E2FT by nearly 2% and by more than 10% when compared with AODV in terms of packet delivery ratio with nearly 30% faulty nodes in the network. The overhead generated by our protocol is lesser by 1% as compared to FTAR and by nearly 17% as compared to E2FT when there are nearly 30% faulty nodes.