A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing

Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the crowdsourcing applications, requesters find it essential to harness the power of collaboration among the workers by forming teams of skilled workers satisfying their complex tasks' requirements. This type of MCS is called Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on the aspect of team skills maximization. Other team formation studies on social networks (SNs) have only focused on social relationship maximization. In this paper, we present a hybrid approach where requesters are able to hire a team that, not only has the required expertise, but also is socially connected and can accomplish tasks collaboratively. Because team formation in CMCS is proven to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces computation time and produces reasonable performance results.Comment: This paper is accepted for publication in 2019 31st International Conference on Microelectronics (ICM

Not Just Silly Cat Videos: Exploring Student Knowledge Sharing via Social Media

Social media have been widely used to share and exchange information for a variety of reasons. Students can use social media to exchange everything from cat videos to important information including Internet knowledge sources for learning. This study seeks to develop a better understanding of how and why students use social media to share Internet knowledge resources for learning. It builds on the theories of social capital and social cognition to develop a model for examining the influence of different dimensions of social capital (i.e., structural, relational, and cognitive) on students’ knowledge sharing via social media. Based on a survey of students at New Jersey Institute of Technology (NJIT), we find that the critical influencers of knowledge sharing are identification and outcome expectations. Results of this research can guide educators seeking to encourage knowledge sharing between learners by identifying the critical issues that motivate and limit such sharing

Transfer Learning-Based Automatic Hurricane Damage Detection Using Satellite Images

After the occurrence of a hurricane, assessing damage is extremely important for the emergency managers so that relief aid could be provided to afflicted people. One method of assessing the damage is to determine the damaged and the undamaged buildings post-hurricane. Normally, damage assessment is performed by conducting ground surveys, which are time-consuming and involve immense effort. In this paper, transfer learning techniques have been used for determining damaged and undamaged buildings in post-hurricane satellite images. Four different transfer learning techniques, which include VGG16, MobileNetV2, InceptionV3 and DenseNet121, have been applied to 23,000 Hurricane Harvey satellite images, which occurred in the Texas region. A comparative analysis of these models has been performed on the basis of the number of epochs and the optimizers used. The performance of the VGG16 pre-trained model was better than the other models and achieved an accuracy of 0.75, precision of 0.74, recall of 0.95 and F1-score of 0.83 when the Adam optimizer was used. When the comparison of the best performing models was performed in terms of various optimizers, VGG16 produced the best accuracy of 0.78 for the RMSprop optimizer

The removal of methylene blue as a remedy of dye-based marine pollution: a photocatalytic perspective

The effluents containing the discarded water from the textile industry are graded as one of the foremost pollutants in all industrial sectors. The wide varieties of dyes, which is susceptible to the possibility of carcinogens or mutagens, and it will be harmful to entire ecosystem. The titanium dioxide, one of the foremost heterogeneous semiconductor photocatalysts, has been acknowledged for the wide applications in hydrogen production from water splitting and degradation of organic and inorganic pollutants since last few decades. The present work is successively advanced for the removal of methylene blue from the seawater. The work was carried under natural sunlight with the presence of C/TiO2 and Cu–C/TiO2. The photocatalytic removal experiment was carried out with different catalyst dosages (0.25–1.25 g/L), different initial concentrations from 5 to 30 μM and at different pH values (3–9). The highest removal rate was found at the optimum condition of pH 8 and 1 g/L. At the optimum condition, 100% efficiency was achieved under natural sunlight. The kinetic studies reveal the pseudo-first-order kinetics and half-life time comparison proves the enhanced visible light harvesting of Cu–C/TiO2