Crowdsourcing contests are contests by which organizations tap into the wisdom of crowds by outsourcing tasks to large groups of people on the Internet. In an online environment often characterized by anonymity and lack of trust, there are inherent uncertainties for participants of such contests. This study focuses on crowdsourcing contests with winner-take-all prizes. During these contests, submissions are made sequentially and contest hosts can provide public in-process feedback to the submissions as soon as they are received. Drawing on the uncertainty literature, we examine how the use of prize guarantees (guaranteeing that a winner will be picked and paid) and in-process feedback (numeric ratings to individual designs and public textual comments during the contest) can help reduce the various uncertainties faced by the contestant, thereby attracting more submissions. We find that guaranteeing the prize increases submissions. The volume of in-process feedback (both numeric reviews and textual comments) has a positive effect on the number of submissions, and such an effect is bigger in contests without prize guarantees. In addition, providing highly positive or extremely negative feedback discourage overall future submissions, and the negative effect of highly positive feedback is mitigated in guaranteed contests.

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The collaborative filtering (CF) based models are capable of grasping the interaction or correlation of users and items under consideration. However, existing CF-based methods can only grasp single type of relation, such as restricted Boltzmann machine which distinctly seize the correlation of user-user or item-item relation. On the other hand, matrix factorization explicitly captures the interaction between them. To overcome these setbacks in CF-based methods, we propose a novel deep learning method which imitates an effective intelligent recommendation by understanding the users and items beforehand. In the initial stage, corresponding low-dimensional vectors of users and items are learned separately, which embeds the semantic information reflecting the user-user and item-item correlation. During the prediction stage, a feed-forward neural networks is employed to simulate the interaction between user and item, where the corresponding pretrained representational vectors are taken as inputs of the neural networks. Several experiments based on two benchmark datasets (MovieLens 1M and MovieLens 10M) are carried out to verify the effectiveness of the proposed method, and the result shows that our model outperforms previous methods that used feed-forward neural networks by a significant margin and performs very comparably with state-of-the-art methods on both datasets.

The objective of private authentication for Radio Frequency Identification (RFID) systems is to allow valid readers to explicitly authenticate their dominated tags without leaking tags' private information. To achieve this goal, RFID tags issue encrypted authentication messages to the RFID reader, and the reader searches the key space to locate the tags. Due to the lack of efficient key updating algorithms, previous schemes are vulnerable to many active attacks, especially the compromising attack. In this paper, we propose a Strong and lightweight RFID Private Authentication protocol, SPA. By designing a novel key updating method, we achieve the forward secrecy in SPA with an efficient key search algorithm. We also show that, compared with existing designs, SPA is able to effectively defend against both passive and active attacks, including compromising attacks. Through prototype implementation, we observe that SPA is practical and scalable in current RFID infrastructures.

Satellite remote sensing provides a promising way to estimate regional evapotranspiration (ET) in a spatially distributed manner. In this study, an enhanced two-source evapotranspiration model for land (ETEML) is proposed based on a trapezoid framework of the vegetation fractional cover and land surface temperature (VFC/LST) space. In ETEML, a VFC/LST trapezoid space is theoretically defined for each pixel, and a pixel-wise mixed surface temperature decomposition method is proposed. ETEML is based on a two-source scheme, and the crop water stress index (CWSI) concept is applied to parameterize the soil evaporation and the vegetation transpiration separately. The proposed model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) site in central Iowa, USA. Evaluation with a remotely sensed dataset from Landsat was carried out to assess the performance of ETEML. Compared with the tower observations, the mean absolute deviation (MAD) and the root mean square deviation (RMSD) for the ETEML estimated latent heat flux (LE) are, respectively, 49 W/m 2 and 59 W/m 2 , comparable to retrieval accuracies published in other studies. Comparison between ETEML and variations on a simpler trapezoid interpolation model (TIM1 and TIM2) indicates that ETEML reduces the subjectivity and uncertainties involved in TIM1 and TIM2. Overall, the results suggest that ETEML is promising and can expand the application of the trapezoid framework-based ET modeling approaches to heterogeneous surfaces.

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Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicle traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts one greedy heuristic to select the most "centric" vehicles as seeds for mobile advertising. Extensive trace-driven simulation results show that POST can greatly improve the coverage and the intensity of advertising.

The objective of private authentication for Radio Frequency Identification (RFID) systems is to allow valid readers to explicitly authenticate their dominated tags without leaking the private information of tags. In previous designs, the RFID tags issue encrypted authentication messages to the RFID reader, and the reader searches the key space to identify the tags. Without keyupdating, those schemes are vulnerable to many active attacks, especially the compromising attack. We propose a strong and lightweight RFID private authentication protocol, SPA. By designing a novel key-updating method, we achieve the forward secrecy in SPA with an efficient key search algorithm. We also show that, compared with existing designs, (SPA) is able to effectively defend against both passive and active attacks, including compromising attacks. Through prototype implementation, we demonstrate that SPA is practical and scalable for current RFID infrastructures.